A computational view on nanomaterial intrinsic and extrinsic features for nanosafety and sustainability

Giulia Manca di a, ⇑ , Alicja Mikolajczyk b,m, ⇑ , Vigneshwa i K. Annapoo ani c , Aileen Bahl d , Kos as Blekos e , Jaanus Bu k , Ya kın A. Çe in g , Kons an inos Chai e akis e , Su apa Du a c , Lau a Esco ihuela g , Ka olina Jagiello b,m , Ankush Singhal h , Rianne an de Pol h , Miguel A. Baña es i , Nicolae-Vio el Buche e c , Monica Cala ayud j , Ve ónica I. Dumi d , Da ide Ga dini k , Nina Jeliazko a l , And ea Haase d ,E fie Ma coulaki e , Benjamí Ma o ell g , Tomasz Puzyn b,m , G.J. Agu Se ink h , Felice C. Simeone k , Kaido Tämm , Eliodo o Chia azzo a, ⇑ a Poli ecnico di To ino, Co so Duca degli Ab uzzi, 24, To ino 10129, I aly b Labo a o y o En i onmen al Chemome ics, Ins i u e o En i onmen al and Human Heal h P o ec ion, Facul y o Chemis y, Uni e si y o Gdansk, 80-308 Gdansk, Poland c School o Physics & Ins i u e o Disco e y, Uni e si y College Dublin, Belfield, Dublin 4, I eland d Ge man Fede al Ins i u e o Risk Assessmen (B R), Depa men o Chemical and P oduc Sa e y, Be lin, Ge many e Ins i u e o Nuclea & Radiological Sciences & Technology, Ene gy & Sa e y, Na ional Cen e o Scien ific Resea ch  nDemok i os_ z, A hens, G eece Ins i u e o Chemis y, Uni e si y o Ta u, Ra ila 14a, 50411 Ta u, Es onia g Depa amen d’Enginye ia Química, Uni e si a Ro i a i Vi gili, A inguda dels Països Ca alans, 26, 43007 Ta agona, Spain h Leiden Ins i u e o Chemis y, Leiden Uni e si y, P.O. Box 9502, 2300 RA Leiden, The Ne he lands i Ins i u e o Ca alysis, ICP-CSIC, Ma ie Cu ie 2, E-28049 Mad id, Spain j Labo a oi e de Chimie Theo ique, CNRS, So bonne Uni e si é, 4 Place Jussieu, 75005 Pa is, F ance k ISTEC-CNR, Ins i u e o Science and Technology o Ce amics - Na ional Resea ch Council o I aly, Faenza, I aly l Ideaconsul L d, Sofia, Bulga ia m QSAR Lab L d., T zy Lipy 3, 80-172 Gdansk, Poland In ecen yea s, an inc easing numbe o di e se Enginee ed Nano-Ma e ials (ENMs), such as nanopa icles and nano ubes, ha e been included in many echnological applica ions and consume p oduc s. The desi able and unique p ope ies o ENMs a e accompanied by po en ial haza ds whose impac s a e di ficul o p edic ei he quali a i ely o in a quan i a i e and p edic i e manne . Alongside es ablished me hods o expe imen al and compu a ional cha ac e isa ion, physics-based modelling ools like molecula dynamics a e inc easingly conside ed in Sa e and Sus ainabili y-by- design (SSbD) s a egies ha pu use heal h and en i onmen al impac a he cen e o he design and de elopmen o new p oduc s. Hence, he u he de elopmen o such ools can suppo sa e and sus ainable inno a ion and i s egula ion. This pape s ems om a communi y e o and p esen s he ou come o a ou -yea -long discussion on he benefi s, capabili ies and limi a ions o adop ing physics-based modelling o compu ing sui able ea u es o nanoma e ials ha can be used o oxici y assessmen o nanoma e ials in combina ion wi h da a-based models and expe imen al assessmen o oxici y endpoin s. We e iew A compu a ional iew on nanoma e ial in insic and ex insic ea u es o nanosa e y and sus ainabili y ⇑ Co esponding au ho s. E-mail add esses: Manca di, G. ([email p o ec ed]), Mikolajczyk, A. ([email p o ec ed]), Chia azzo, E. ([email p o ec ed]). 344 1369-7021/Ó2023 Published by Else ie L d. This is an open access a icle unde he CC BY-NC-ND license (h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/). Ma e ials Today dVolume 67 dJuly/Augus 2023 RESEARCH RESEARCH: Re iew mode n mul iscale physics-based models ha gene a e ad anced sys em-dependen (in insic) o ime- and en i onmen -dependen (ex insic) desc ip o s/ ea u es o ENMs (p ima ily, bu no limi ed o nanopa icles, NPs), wi h he o me being ela ed o he ba e NPs and he la e o hei dynamic finge p in ing upon en e ing biological media. The ocus is on (i) e ec i ely ep esen ing all nanopa icle a ibu es o mul icomponen nanoma e ials, (ii) gene a ion and inclusion o in insic nano o m p ope ies, (iii) inclusion o selec ed ex insic p ope ies, (i ) he necessi y o conside ing dis ibu ions o s uc u al ad anced ea u es a he han only a e ages. This e iew enables us o iden i y and highligh a numbe o key challenges associa ed wi h ENMs’da a gene a ion, cu a ion, ep esen a ion and use wi hin machine lea ning o o he ad anced da a-d i en models o ul ima ely enhance oxici y assessmen . Finally, he se up o dedica ed da abases as well as he de elopmen o g ouping and ead-ac oss s a egies based on he mode o ac ion o ENMs using omics me hods a e iden ified as eme ging me hodologies o sa e y assessmen and educ ion o animal es ing. Keywo ds: Nanoin o ma ics; Nanosa e y; Enginee ed nanoma e ials; Physicochemical desc ip o s; Ma e ials modeling; Machine lea ning; G ouping app oaches; Mul iscale modeling; Sa e and Sus ainabili y-by-design(SSbD) In oduc ion ENMs pe o m key dedica ed asks in ca alysis [1], medicine [2,3], ag icul u e [4], ood [5] and ene gy [6,7] among many o he s, because o hei excep ional and o en unique cha ac e is ics. ENMs unde pin new p oduc s and de ices equi ing con ol o ma e a he nanome e scale, and he possibili y o fine- une hei syn hesis p o ocols esul s in coun less a ian s wi h di e - en physicochemical p ope ies. The excep ional p ope ies o ENMs - s emming om he manipula ion o ma e a he a omic scale - can also come wi h ye li le-known isks o human heal h and he en i onmen . In ac , he biological ac i i y o ENMs is hough o be closely ela ed o hei physicochemical cha ac e - is ics, which may be al e ed by he biological medium i sel du - ing he li e ime o ENMs (e.g., p o ein co ona o ma ion and s uc u al modifica ions). Bo h he in insic and ex insic physic- ochemical ea u es o ENMs a e key o ecognizing and p edic - ing haza ds as well as assessing sa e y cha ac e is ics. The o me a e ela ed o chemical composi ion, c ys al s uc u e, size, shape and su ace s uc u e, whe eas he la e pe ain o he non- i ial in e ac ion wi h he en i onmen . As such, a comple e se o hose ea u es (he e e e ed o as ad anced desc ip o s) a e c i ical in nanoin o ma ics o da a-based model de elopmen , such as he popula quan i a i e nanos uc u e–ac i i y/p ope y models (QSAR/QSPR). In his espec , p edic i e models o e unp ece- den ed oppo uni ies o knowledge-based op imiza ion and de elopmen o new ENMs imp o ing hei unc ionali y and - a he same ime - minimizing unexpec ed heal h and/o en i- onmen al isks. P elimina y in silico sc eening o possible e - sions o new ENMs can hus lead o op imal nanos uc u es wi h educed haza dous cha ac e is ics e en be o e he p oduc- ion s age. Howe e , he pace o u he p og ess in nano echnol- ogy will c i ically depend on a syne gis ic knowledge in eg a ion o expe imen al e idence wi h da a om eliable heo e ical and compu a ional models. The combined s udy o ma e ials modeling (nanos uc u e cha ac e iza ion) and p edic i e models o he design o sa e Nomencla u e AIMD ab ini ioMolecula Dynamics AO(P) Ad e se Ou come (Pa hway) API Applica ion P og amming In e ace BD B ownian Dynamics CG(MD) Coa se-G ained (Molecula Dynamics) CSS Chemicals S a egy o Sus ainabili y DFT Densi y Func ional Theo y DFTB Densi y Func ional Tigh -Binding DLVO De jaguin-Landau-Ve wey-O e beek ENM Enginee ed NanoMa e ials FAIR Findable, Accessible, In e ope able and Reusable GA Gene ic Algo i hm GAN Gene a i e Ad e sa ial Ne wo k HOMO Highes Occupied Molecula O bi al hPF hyb id Pa icle-Field KE Key E en s LCA Li e Cycle Assessmen LDM Liquid D op Model LUMO Lowes Unoccupied Molecula O bi al MD Molecula Dynamics MDReaxFF Reac i e Fo ce Field Molecula Dynamics MIE Molecula Ini ia ing E en s ML Machine Lea ning MoA Mode o Ac ion MODA MOdelling DA a fiche NAM New App oach Me hodologies PMF Po en ial o Mean Fo ce QM Quan um Mechanics QNAR Quan i a i e Nanos uc u e–Ac i i y Rela ionship QSAR Quan i a i e S uc u e–Ac i i y Rela ionship QSPR Quan i a i e S uc u e–P ope y Rela ionship RNN Recu en Neu al Ne wo k S(S) bD Sa e (and Sus ainable) by Design SASA Sol en Accessible Su ace A ea SCFT Sel -Consis en Field Theo y VAE Va ia ional Au oencode RESEARCH: Re iew Ma e ials Today d Volume 67 d July/Augus 2023 RESEARCH 345 nanos uc u es wi h desi ed p ope ies (Sa e and Sus ainabili y- by-design pe spec i e, SSbD) b ings along new oppo uni ies bo h in he academic and indus ial con ex . None heless, signi - ican challenges a ising om bo h he ex emely demanding compu a ional models (physics-based and da a-based) and he p ac ically unlimi ed numbe o possible combina ions o di e - en subs ances and nano o ms ha lie ahead. E en o a single ma e ial, he e is a ple ho a o possible bulk and su ace s uc- u es, de ec s and e mina ions, each deli e ing unique p ope - ies. This gene al challenge can only be ackled by an in eg a ed app oach. He e, we c i ically analyze he p ocess asso- cia ed wi h he in silico e alua ion o he ENMs desc ip o s, wi h he aim o highligh ing challenges and oppo uni ies when using physics-based modelling o gene a ing hem. Ou discussion akes place unde he b oad pe spec i e o he Eu opean and US legisla ion de elopmen and hei eme ging s a egies ela ed o isk assessmen o nanoma e ials (e.g., he EU Ma e ials Modelling Council, he EU REACH egula ion, US- EU nanoEHS ini ia i e, he EU-US Nanoin o ma ics Roadmap 2030[8]). Despi e he well-accep ed ela ionship be ween physic- ochemical p ope ies (i.e. desc ip o s) and he (eco)- oxicological endpoin s, a comp ehensi e compu a ional desc ip ion o ENMs and an unde s anding o he basic mechanisms behind hei in e ac ion wi h biological media a e s ill e y challenging. Fi s , we ocus on some o he (physics-based) compu a ional app oaches o es ima ing ad anced desc ip o s a ime and space scales ele an o nanosa e y. In his espec , we p o ide an o e iew o he app oaches o in es iga ing ENMs elec onic and a omis ic s uc u e ( hus ocusing on in insic ea u es) up o he mesoscopic desc ip ion o in e ac ions wi h biological ma - e , such as p o eins o cell memb anes, hence mo ing owa ds mo e ex insic ea u es. Second, mo e ecen da a-based models and app oaches o nanosa e y assessmen a e analyzed. As illus a ed schema ically in he op pa o Fig. 1, compu a ions can be pe o med a di e - en space/ ime scales by sol ing app op ia e physical model equa ions wi h ad anced desc ip o s collec ed om each simula- o . Ad anced desc ip o s can be passed ac oss di e en ime and space scales, ealizing a chain o mul iscale simula ions [9]. This ision is ce ainly ascina ing, howe e , as discussed below, i comes wi h o midable challenges: as opposed o he high- accu acy da a ex ac ed by he physics-based models, he inhe - en compu a ional cos is o en p ohibi i ely high. This leads o a low da a a iance ha ende s he subsequen ansla ion FIG. 1 TOP: Physics-based models ensu e high ideli y a a high compu a ional cos , making i possible o in es iga e only a es ic ed egion o he ENM design space. Hence, he necessa y a iance is needed o desc ibe he la ge a ie y o pa ame e s o in e es (e.g., pa icle size, coa ing, e c.). Thus, p edic ing ad anced desc ip o s o nanosa e y has o s em om di e en sou ces, such as li e a u e da a and c ude app oxima ion models cha ac e ized by a lowe ideli y le el. BOTTOM: Disc epancy in ideli y and a iance is o be p ope ly o ches a ed, possibly using ML models [11]. RESEARCH: Re iew RESEARCH Ma e ials Today d Volume 67 d July/Augus 2023 346 o such da a in o inpu o QSAR/QSPR (o o he da a-based) models ex emely di ficul i no impossible. De ails on obs acles a e e y ele an scale a e discussed, and he cu en s a us and challenges wi h he Eu opean egula o y con ex on ma e ials modeling and in silico es ima e o desc ip o s o nanosa e y pu - poses a e epo ed below [10]. Sa e and Sus ainabili y-by-design (SSbD) s a egy: F om nano o m desc ip ion o sa e y modeling Wi hin he Eu opean G een Deal, he Chemicals S a egy o Sus- ainabili y (CSS)[12] iden ified se e al ac ions o educe nega i e impac s on human heal h and he en i onmen associa ed wi h chemicals, ma e ials, p oduc s, and se ices comme cialized o in oduced on o he EU ma ke . In pa icula , he ambi ion o he CSS is o phase ou he mos ha m ul subs ances and subs i- u e, as a as possible, all o he subs ances o conce n and o he - wise minimize hei use and ack hem. This objec i e equi es no el app oaches o analyze and compa e all li e cycle s ages, e ec s, eleases, and emissions o specific chemicals, ma e ials, p oduc s, and se ices, and mo ing owa ds ze o pollu ion o ai , wa e , soil, and bio a. The SSbD amewo k aims o suppo he design and de elopmen o sa e and sus ainable chemicals and ma e ials wi h esea ch and inno a ion (R&I) ac i i ies. Al hough he sa e y o nanoma e ials has been o conce n o he scien ific communi y o mo e han wo decades, he e is s ill a limi ed numbe o alida ed and egula o y-accep ed al e na- i e nano-specific app oaches o assessing hei human sa e y. In ac , he cu en knowledge abou a ious ad e se e ec s induced by nanoma e ials a e exposu e does no ye enable a b oad de elopmen o he SSbD s a egy. The sa e y and sus ainabili y assessmen Reliable and e ficien me hods allowing, in a imely manne , o assess exposu e isks should be fi s de eloped. In his con ex , he de elopmen o new al e na i e me hods combining in i o, chemical analysis as well as in silico models o p edic he po en ial ad e se impac o chemicals, including nanoma e- ials, on human heal h is highly needed [13]. These ools a e expec ed o be use ul o egula o s and policymake s; he e o e, hey need o conside biologically plausible and egula o y- ele an e en s essen ial o he occu ence o possible ad e se ou comes [14]. In line wi h his poin o iew is he s a egy ha in eg a es nanoin o ma ics models wi h he Ad e se Ou come Pa hways (AOPs) [15,16]. The AOP is a amewo k ha desc ibes a sequence o biological e en s ollowing s esso exposu e and leading o a ious Ad e se Ou comes (AO). This concep links Molecula Ini ia ing E en s (MIE) o he se ies o key cellula - o issue-le el changes (so-called Key E en s, KE) ha culmina e in he mani es a ion o AO. By suppo ing he iden ifica ion o AOP- ele an e en s and in o ma ion ha migh be applied o he weigh o e idence-based sa e y decisions, AOP can se e as a amewo k o de eloping nanoin o ma ics models use ul o egula o y ac ions. This would mean ha e en s ecognized as c ucial o mani es nano-specific ad e se e ec s should be con- side ed o modeling. As an example, ecen ly, Jagiello e al. [15] p oposed a model ha linked he s uc u al p ope ies o mul iwalled ca bon nano ubes wi h he ec ui men o p o- inflamma o y media o s in o he lungs, ha finally leads o lung fib osis acco ding o AOP173. Applying he AOP amewo k in his model enables o unde s and e en s igge s ollowing nan- o ube exposu e and occu ing in di e en biological o ganiza- ions (molecula , cellula , issue, o gan, indi idual). To sum up, wi h he de elopmen o he AOP-ancho ed nanoino o ma ics models (models o key biologically plausible and egula o y- ele an e en s), he usage and accep ance o hose app oaches in making egula o y decisions abou he sa e y o nanoma e ials exposu e can inc ease. Addi ionally, he JRC (2022)[17] e iewed p e ious decision amewo ks o ma e ials sa e y o in es iga e how sus ainabili y (b oadly including social and economic aspec s) was conside ed and o define a se o c i e ia o in eg a e sa e y and sus ainabil- i y. Sus ainabili y assessmen can use con en ional sus ainabili y me ics adap ed o nano echnology p oduc s and p ocesses. In his con ex , S iebe o a e al. [18] used echno-economic and li e cycle en i onmen al c i e ia o sus ainabili y assessmen , o compa e al e na i e nanopa icle p oduc ion echnologies; Ga cía-Quin e o and Palencia [19] analyzed con en ional quan- i a i e sus ainabili y me ics and p oposed Li e Cycle Assess- men (LCA) as a sui able me ic o compa e, op imize and quan i y bio-based nanobio echnology and nanosyn hesis p o ocols. Regula o y ac ions o d i e SSbD chemicals and ma e ials P edic i e in silico modeling, accessible and sea chable da abases and quan i a i e ools o isk assessmen and p e en ion a e c i - ical o he success ul implemen a ion o SbD/SSbD s a egies in he nanosa e y con ex and o de elop ele an p o ocols, e e - ence ma e ials, ealis ic in i o and compu a ional models, as well as g ouping and ead-ac oss me hods. [20] Addi ional chal- lenges s em om he need o assess he a e and eac i i y o nex gene a ions o nanoma e ials [21] including sma nanoma e ials and nano-enabled p oduc s [22]. New App oach Me hodologies (NAMs), comp ise a wide ange o such models and ools [23] o conduc obus and eliable chemical sa e y assessmen s and educe animal es ing. Wi h subs an ial unding, he Eu opean Commission has sup- po ed NAMs o nanoma e ials, Re s. [24,25], acknowledging hei po en ial in egula o y decision-making and inno a ion. NAMs could inc ease egula o y p epa edness h ough fi - o - pu pose da a and s anda dized es s specific o nanoma e ials [26], and hei po en ial would be be e exploi ed wi hin ie ed egula o y schemes. [27] Conside a ion o a ailable s anda ds (as he OECD s anda d o QSARs [28] and ecommenda ions o he Eu opean Ma e ials Modelling Council o alida ing in silico ools is essen ial o p omo e hei adop ion o egula o y use. On he echnical side, his also enables model in eg a ion in o comple e compu a ional IATA wo kflows [29]. As p esen ed in Sec ion ‘Da a-based models o linking ENM ea u es o sa e y’, he e is ich li e a u e on a a ie y o models o he p edic ion o ENMs p ope ies and oxici y. The e e se p oblem, i.e. he de elopmen o NM s uc u es ea u ing desi ed o op imal p ope ies has also been conside ed in ew ecen wo ks. As a ep esen a i e example, p elimina y wo ks consid- e ed a condi ional deep con olu ional Gene a i e Ad e sa ial Ne wo k (GAN) using compe i i e lea ning o sugges nanopho- RESEARCH: Re iew Ma e ials Today d Volume 67 d July/Augus 2023 RESEARCH 347 onic s uc u es wi h desi ed op ical p ope ies [30], and a GAN o gene a e c ys alline po ous ma e ials, in pa icula pu e silica zeoli e s uc u es [31]. Howe e , since he e is s ill much o do o unde s anding he beha io o ENMs, se e al p ojec s ha e been unded by he EU in he las yea s [32–35] o add ess sa e y and isk go e nance issues. Wi h his in mind, esea che s in NanoIn o maTIX [36] p o- jec ha e de eloped an op imiza ion me hodology o guide he sea ch o sa e ENMs and o suppo he applica ion o sa e( )- by-design (SbD) app oaches [37]. The assessmen and sc eening p ocesses use e ficien ep esen a ions coupled wi h quan i a i e ools o simila i y assessmen o in es iga e mo phology-based beha io [38]. The now implemen ed ool can be ained using a ailable da ase s and can enable he assessmen o a ious design op ions gene a ed du ing he op imal sea ch. Haza ds o ENMs As men ioned abo e, he physicochemical p ope ies o nanoma- e ials may significan ly di e om hei bulk coun e pa s. Despi e he beneficial echnological consequences associa ed wi h his, ENMs migh exhibi haza dous e ec s on human heal h o he en i onmen . The expe ience ga he ed om he employmen o asbes os fibe s decades ago se es as a cau iona y ale o he po en ial haza d o nanoma e ials. Asbes os we e ex ensi ely used in a ious p oduc s wi h a wide ange o appli- ca ions because o he ad an ageous p ope ies, like hea esis- ance/isola ion and du abili y. Howe e , a e many yea s o ubiqui ous quo idian p esence, he insidious ha m ul e ec s on heal h became e iden . In his ega d, i was disco e ed ha a e a long la ency pe iod asbes os we e able o cause lung cance and meso helioma [39]. This e ec was linked wi h i s high-aspec a io fibe s uc u e and us a ed phagocy osis. Ga he ing ma e- ials by common ea u es leading o simila ad e se e ec s is a fi s s ep o g oup and ead-ac oss. The e o e, in es iga ing he po en ial haza ds o nanoma e ials is c ucial o gua an ee hei sa e y enabling hei esponsible usage. Unde s anding hei po en ial haza d is impo an o enginee ele an modifica ions and unde ake he necessa y measu emen s o minimize expo- su e, while s ill p ofi ing om hei a ious beneficial p ope ies. Unde s anding hei po en ial haza d equi es desc ibing su ace s uc u e, p ope ies and eac i i y, which define how hese engi- nee ed nanoma e ials in e ac wi h each o he and wi h he en i- onmen . The la e de e mines he end o agg ega e, which a ec s a e and exposu e; mo eo e i shapes hei su ace p op- e ies, hus al e ing in e ac ion among pa icles ( a e, exposu e) and modi ying eac i i y. Al hough he e a e se e al exposu e scena io o nanoma e ial oxici y, inhala ion is conside ed he mos c i ical up ake ou e since i allows he pa icles o each he sensi i e issues deep wi hin he lungs [40]. The e, he pa i- cles could be aken up by lung cells o in e ac wi h he immune sys em, leading o ha m ul e ec s. Asbes os is no he only 1- dimensional ma e ial po en ially ha m ul. O he ma e ials, wi h e y di e en chemis y and composi ion, sha e common ea- u es. S udies on specific ypes o ca bon nano ubes sugges ed ha hey may show simila oxici y as asbes os fibe s a lung le el [41,42]. Simila ly, esea ch on sil e nanopa icles showed geno oxici y and poin ed ou ha smalle -sized pa icles exhib- i ed highe oxici y in in i o se ings [43]. I is impo an o emphasize ha no all nanoma e ials a e associa ed wi h isks o human heal h. Thei oxicological po en ial depends on di - e en ac o s such as size, shape, unc ionaliza ion, besides chemical composi ion. Ul ima ely, oxic e ec is a phenomenon igge ed a he su ace o nanoma e ials, and how i in e ac s wi h i s en i onmen . Bulk p ope ies do no ypically co ela e wi h su ace p ope ies o s uc u e; he e o e, no di ec ansla- ion o bulk cha ac e is ics can be made o su ace eac i i y. Expe imen al cha ac e iza ion may be hampe ed by his, which mus be aken in conside a ion when defining cha ac e iza ion s a egies. F om a mo e me hodological pe spec i e, ma e ial cha ac e iza ion should include de ailed in o ma ion on he su - ace and i s de ec s, how he la e and he o me depend on he unde lying bulk s uc u e and de ec s and how hose a e a ec ed and, ul ima ely, shaped by he in e ac ion wi h he en i onmen o biological media. The combina ion o hese p ope ies esul s in coun less possible nanoma e ials ha can en e he ma ke , which can apidly o e whelm he cu en isk assessmen p ocedu e. Fo his eason, SSbD s a egies a e pa icula ly impo an , as hey ha e he po en ial o acili a e and expedi e isk assessmen , necessa y o minimize he po en ial isks associa ed wi h he use o nanoma e ials h oughou hei li ecycle. Mo eo e , SSbD s a egies a e c ucial o ensu e he esponsible de elopmen o nanoma e ials and hei applica ions. The ole o ma e ials modeling in ENMs’haza d assessmen I is wo h s essing ha accu a e physics-based modelling o ma e ials is ypically no mean o di ec ly simula e he basic mechanisms unde pinning ENMs oxici y. On he con a y, such ools and me hods can be used o compu e p ope ies ha can be e cap u e he complexi y o ENMs composi ion and he influence o ex e nal condi ions on oxici y, as opposed o di fi- cul , ime-consuming and expensi e expe imen s (when expe i- men s a e possible). Despi e he benefi s o using ma e ials modelling o calcula e nano-desc ip o s, e en un il a ew yea s ago, he scale and complexi y o sys em simula ions we e chal- lenging, and he e was a sho age o models o p edic impo an p ope ies, such as he NM dissolu ion a e [8]. One may hus conclude ha i is highly desi able o base nanosa e y assessmen upon accu a e in insic and ex insic p ope ies, i.e. ea u es ha ep esen a pa icula s uc u e and chemical na u e o he ENMs o in e es and how hese p ope - ies a ec o a e a ec ed by hei (biological) en i onmen . In he ollowing, such ea u es o ad anced desc ip o s and hei e al- ua ion by means o physics-based models is discussed in mo e de ail. In e pola ion- o ex apola ion-based me hods like QSAR and ML will be se ed by a de e minis ic calcula ion o such ad anced desc ip o s in a pa o he pa ame e space ha is expensi e o ha d o assess expe imen ally, o when expe imen- al da a a e unclea o incomple e. On he o he hand, physics- based models may also p o ide di ec insigh in o he ela ion be ween obse ed ensemble p ope ies (phenomena) and sys em pa ame e s (simple and ad anced desc ip o s) ha s ems om a sys ema ic compu a ional sc eening o one o mo e well-defined design pa ame e s. Examples o phenomena conside ed in his e iew a e p o ein abso p ion, NP agg ega ion and memb ane binding. An ins ance o di ec insigh gained by compu a ional RESEARCH: Re iew RESEARCH Ma e ials Today d Volume 67 d July/Augus 2023 348 means is he finding ha small nanopa icles (d, wi h d he memb ane hickness, usually 4–5 nm) can simply pe mea e h ough he memb ane, simila o small molecules, while la ge nanopa icles (d) will be ully engul ed o w apped by he memb ane upon binding. The binding cha ac e is ics o nanopa icle sizes compa able o he memb ane hickness is s ill unclea [44]. A sepa a ion o desc ip o s in o in insic and ex insic p o- ides a s aigh o wa d basis o he selec ion o he mos app o- p ia e physics-based model. Sec ion ‘Da a-based models o linking ENM ea u es o sa e y’discusses he cu en knowledge on ENMs and he (addi ional, ad anced) ea u es ha need o be conside ed, as well as he p og ess on nanoma e ial ep esen- a ions and da a-based models, like nano-QSARs, ha can ecei e and p ocess he calcula ed nano-desc ip o s. Da a-based models o linking ENM ea u es o sa e y The c i ical ole o a gene al ep esen a ion o ENMs In he a emp o gaining a deep a ionale om da a linking ENMs and hei obse ed sa e y p ope ies, an essen ial p e equisi e is he abili y o ep esen complex ma e ials in well s uc u ed and machine- eadable o ma . Un o una ely, o da e, he lack o a s anda dized seman ic cha ac e iza ion o he s uc u al ENMs ea u es and en i onmen al a iables makes i di ficul o agg e- ga e, cu a e and e alua e da a om di e en sou ces and o use hem o simula ions o o aining new (da a-d i en) models; hus making he meaning ul in eg a ion o da ase s a e y demanding ask. Web da abases and eposi o ies o chemical subs ances (e.g., ChEMBL, PDBe, ZINC15, Pubmed e c.) use s an- da dized linea no a ions o subs ance iden ifica ion (SMILES, SYBYL Line No a ion, o InChI) [45,46]. These no a ions a e also employed in deep lea ning ools o guide he gene a ion o easi- ble molecula s uc u es wi h desi able p ope ies (e.g., Gene a- i e Ad e sa ial Ne wo ks (GANs), Va ia ional Au o-Encode s (VAEs), Recu en Neu al Ne wo ks (RNNs), e c.). The ex ension o hese no a ions o polyme s, mix u es, eac ions, e c. has also been p oposed, bu ENMs en ail addi ional challenges compa ed o con en ional chemicals. The key p ope ies o ENMs ha e a s ong dependence on he physical and s uc u al ea u es. ENMs cha ac e is ics such as he spa ial ela ionship be ween compo- nen s, hei ela i e sizes, e c., all play an impo an ole in hei inhe en and eme gen p ope ies. Wha is mo e, many o hese p ope ies a e en i onmen -dependen , as hey a e a ec ed by a ious ex e nal ac o s such as empe a u e and concen a ion h ough non-linea dependencies, making ENMs desc ip ions e en mo e sub le and complica ed. A comple e ENM ep esen a ion, he e o e, would equi e he inclusion o in o ma ion on many mo e aspec s han wha cu - en linea no a ions p o ide. Ex ending he cu en ep esen a- ions o include such ad anced in o ma ion con en is no a s aigh o wa d ask. Lynch e al. [47] ha e ini ia ed an ex ensi e discussion among a ious s akeholde s and p oposed a ame- wo k o an InChI s anda d applied o ENMs as well as a oadmap o i s de elopmen . They aimed o add ess he a ie y o com- plex nanos uc u es, using a hie a chical app oach ha in o- duces new laye s on he InChI no a ion o he size, shape, c ys al s uc u e, and ligand binding o he ENM, and, possibly, ex insic and su ace p ope ies. Recen ly, Blekos e al. p oposed p inciples o a mo e accu a e, comple e, flexible and inc emen- al ep esen a ion app oach. The p inciples we e demons a ed h ough he de elopmen o ex ensions o nano-InChI o encode mo phology/mix u e p ope ies and s a is ical dis ibu ions o p ope ies and o s o e me ada a and enable hei euse. The p oposed ep esen a ion amewo k could also p o ide he heo e ical backg ound o g adually cap u e he eal pa icle dynamics unde specific en i onmen al condi ions. [38] These a e cu en ly unde conside a ion in he Nanoma e ials InChI Wo king G oup (h ps://www.inchi- us .o g/nanoma e ials/) and hei p oposal o a new InChI s anda d. A hope o he nea u u e is ha an inc easing numbe o ad anced desc ip o s - as hose discussed below in Sec ion ‘Physics-based models o nanos uc u e cha ac e iza ion’- will be g adually inco po a ed in o such s anda d no a ions. Da a-based models o sa e y assessmen In nanoin o ma ics, popula da a-based models include Quan i- a i e S uc u e Ac i i y Rela ionship modeling (so-called nano- QSAR/-QSPR) ha u ilizes Machine Lea ning (ML) and a ificial in elligence o p edic he desi ed esponse (e.g., biological ac i - i y, oxici y endpoin s o any physicochemical p ope y o in e - es ). Those app oaches a e based on a se o compu a ional and/ o expe imen ally de eloped desc ip o s ep esen ing nanopa i- cle s uc u e. As a ep esen a i e example, Wy zykowska, Mikola- jczyk, e al. [48] p oposed a concep in which a nanos uc u e is cha ac e ized by a iad ha desc ibes: (i) molecula s uc u e; (ii) molecula desc ip o s; (iii) molecula p ope ies ha co espond o chemical composi ion and chemical s uc u es o i s compo- nen s. The p oposed iad co e s he cha ac e iza ion o he in insic p ope ies o nanopa icle s uc u e (so-called sys em- independen o in insic (nano-) desc ip o s (S-desc ip o s in Fig. 2, le panel). [49]. Recen ad ancemen s in nano-QSAR modeling ha e in o- duced hyb id models ha enhance p edic i e capabili ies by in eg a ing mul iple modeling app oaches. These hyb id models o en combine molecula dynamics simula ions da a wi h machine lea ning echniques o be e unde s and and p edic he complex beha io o nanoma e ials in biological sys ems and hei in e ac ions wi h he en i onmen .[50,51] Al hough QSAR/QSPR can be ce ainly ega ded as aluable ools o com- plemen expe imen al s udies on chemicals and nanoma e ials, hey come wi h mul iple challenges ha should be ca e ully ana- lyzed. In pa icula , he e is an absence o comp ehensi e me h- ods o cha ac e ize nanopa icles, which a e essen ial o a s anda d QSAR p ocedu e o a dependable compu a ional app oach ha accu a ely ep esen s he unique ea u es o nanos uc u es –in sho , a lack o us wo hy nano-desc ip o s. Fu he mo e, depending on he su ounding en i onmen , ENMs ea u es may change; hus, he da a-based models should be de eloped based no only on he cha ac e iza ion o ENMs chemical composi ion/chemical s uc u es o i s componen s bu also on he influence o he en i onmen (including expe i- men al condi ions, o biological medium). In o he wo ds, he so-called sys em-dependen (ex insic) nano-desc ip o s o he en i onmen (E-desc ip o s) should also be conside ed o desc ibe nanos uc u e as a whole (Fig. 2, igh panel) [48,49]. RESEARCH: Re iew Ma e ials Today d Volume 67 d July/Augus 2023 RESEARCH 349 In he ollowing wo subsec ions, we p o ide a b ie c i ical e iew o in insic and ex insic desc ip o s in p epa a ion o he mos ecen and ad anced compu a ional app oaches o es i- ma e hem, as discussed in de ail in Sec ion ‘Physics-based mod- els o nanos uc u e cha ac e iza ion’. Sys em-independen in insic ea u es Sys em-independen (in insic) nano-desc ip o s e e o he com- posi ion, componen s’s uc u es and p ope ies ha may be mea- su ed o calcula ed unde a well-defined, unchanging se o condi ions. A fi s example o ad anced desc ip o s ha we e suc- cess ully applied o da a-based modeling o nanopa icles was de eloped based on quan um mechanical calcula ions (so-called QM Desc ip o s) [52]. The QM Desc ip o s ha desc ibe he co e chemis y o he s uc u e we e p oposed in 2011 by Puzyn e al. [52] QM desc ip o s eflec he elec onic o m o a chemical compound. They a e ob ained by applying quan um mechanics o he app op ia e molecula model o an ENMs s uc u e. Du ing he las 10 yea s, di e en esea ch g oups ha e been wo king o de elop mo e sophis ica ed ypes o desc ip o s ha a e no ela ed o de ailed a omis ic simula ions. Fo example, in 2012, To opo e al. [53] p oposed SMILES-based op imal desc ip o s based on he encoded one-, wo- and h ee-elemen SMILES a i- bu es o a compound and can be calcula ed wi h he CORAL so - wa e [54]. This idea was hen ex ended o he simplex ep esen a ion o molecula s uc u e (SiRMS). He e, ano he ype o desc ip o based on he Liquid D op Model (LDM desc ip o s) was p oposed by Sizochenko e al. in 2014 [55]. The me hodology o LDM desc ip o s calcula ion assumes ha an ENM can be ep- esen ed as a sphe ical d op in which elemen a y molecules a e igh ly packed. A he same ime, he densi y o clus e s is equal o he pa icle mass densi y [56]. The p oposed me hodology is based on he he modynamically mos s able uni cell o he con- side ed c ys al s uc u e, ha is eplica ed in h ee dimensions. A e wa ds, a sphe ical ENM is c ea ed by emo ing all a oms ou - side he indica ed diame e . This is a clea simplifica ion: In ac , bulk-cu su aces will es uc u e, and e en a sphe ical pa icle may exhibi egula egions as well as a a ie y o de ec s. [57]. Mo eo e , su ac an s may eo de he su ace di e en ly [58]. Clea ly, du ing he las decade, compu a ional scien is s and nanoin o ma icians ac i e wi hin he Eu opean sa e y commu- ni y ha e made a conside able e o o in eg a e knowledge om exis ing EU comple ed and ongoing p ojec s wi hin EU FP7 and HORIZON 2020 o de elop a mo e comp ehensi e app oach o nanos uc u e cha ac e iza ion [59]. Howe e , we should also ec- ognize ha se e al key challenges ela ed o he app op ia e ep- esen a ion and desc ip ion o ENMs s uc u e a e s ill ahead. Among o he s, hose aspec s ha e been highligh ed by esul s p o ided by Mikolajczyk e al., Wy zykowska, Mikolajczyk, e al. [60,49]. As a as in insic p ope ies o ENMs a e conce ned, one o he main challenges is ela ed o he desc ip ion o he complexi y o nanoma e ials composi ion, hus clea ly equi ing he de elopmen and use o mo e ad anced compu a ional ools capable o e alua e/es ima e desc ip o s ha a e di ficul o e en impossible o access by cu en expe imen s. While such aspec s a e discussed in de ail below wi hin Sec ion ‘Physics-based models o nanos uc u e cha ac e iza- ion’, an addi ional c i ical poin , pa ly ela ed o he la e com- pu a ional assessmen o in insic ENMs ea u es, is ela ed o he lack o de ailed ma e ial cha ac e iza ion in he published li e a- u e. Too o en, indeed, only nominal composi ion, shape and size o nanoma e ial a e epo ed, wi h hei possible coa ing only aguely (i a all) defined. Clea ly, hose unce ain ies add e en mo e complexi y and make benchma king o compu a- ional ma e ial modeling pa icula ly di ficul . Sys em-dependen ex insic ea u es While using QSAR/QSPR o o he da a-based models, a second challenge conce ns ep esen ing he influence o ex e nal condi- ions (su ounding en i onmen ) [48]. A ecen ly published FIG. 2 In insic and ex insic desc ip o s: In insic desc ip o s (i.e. sys em-independen , some imes also e e ed o as S-desc ip o s) e e o he composi ion, componen s’s uc u es, and p ope ies ha may be measu ed o calcula ed unde a well-de ined, unchanging se o condi ions. On he o he hand, ex insic desc ip o s (i.e. sys em-dependen , some imes also e e ed o as E-desc ip o s) ep esen he in luence o he su ounding en i onmen , and may be a ying in ime. See ex and Sec ion ‘Physics-based models o nanos uc u e cha ac e iza ion’ o mo e de ails.. RESEARCH: Re iew RESEARCH Ma e ials Today d Volume 67 d July/Augus 2023 350 s udy [49] indica es ha he sys em-dependen (ex insic) nano- desc ip o s, also e e ed o as en i onmen desc ip o s (E- desc ip o s), a e much mo e c i ical o con olling and manag- ing ou he p ope ies o ENMs han nanos uc u e cha ac e is- ics hemsel es. Thus, in addi ion o s anda d cha ac e iza ion, he expe imen alis should p o ide in o ma ion abou changes in he s uc u e o he nanopa icles depending on he en i on- men ( he su ounding condi ions). As a esul , nex o he co e and coa ing, su ace p ope ies such as p o ein co ona o ma ion (so-called “biomolecula co ona”) play an essen ial ole in cha - ac e izing ENMs’beha io and may be conside ed i s finge p in in a biological medium, see also Sec ion ‘Ex insic ad anced desc ip o s: Mesoscopic le el’below o mo e de ails. The sys em-dependen (ex insic) nano-desc ip o s a e c ucial in desc ibing physicochemical p ope ies such as elec opho e ic mobili y o ze a po en ial alue unde specified condi ions, eflec ing he hyd ophobici y, biomolecula co ona, dissolu ion a e, so p ion, su ace eac i i y, deg ee o agg ega ion/agglome - a ion, o ENM pe sis ence. De eloping he sys em-dependen (ex insic) nano- desc ip o s is challenging because he nanos uc u e may change du ing i s li e ime due o i s anspo h ough di e en en i on- men s. In ac , nano-bio in e ac ions a e in p inciple d i en by he nanoma e ials a e in biological and en i onmen al compa - men s, meaning how ma e ials ansloca e, ac as ca ie s o u - he oxican s and finally come in ouch wi h biological a ge s. Unde his pe spec i e, he su ace cha ge and we abili y a e conside ed he key de e minan s o he a e and beha io o nanoma e ials dispe sed in he exposu e media. Fu he mo e, he elec os a ic in e ac ions ha keep pa icles dispe sed, p e en ing o p omo ing con ac wi h cell memb anes, depend on he su ace po en ial shown a he slipping plane (Ze a po en ial), as well as o he impo an p ope ies ha d i e nano- bio eac i i y such as hyd ophilici y o he su ace in e ac ion wi h biomolecules solubilized in he media. In his espec , he iden ifica ion o he pH a which he Ze a po en ial is equal o ze o (isoelec ic poin ) allows making hypo hesis on he ype o acid/base beha io o su aces and on he p esence o cha ged molecules specifically adso bed, as well as on he colloidal s abil- i y o nano-dispe sed phases and o he occu ing o he e o- agg ega ion phenomena [60–63]. In addi ion, he Ze a po en ial is e y use ul o he design op imiza ion o su ace unc ionaliza- ion s a egies applied o con ol nanopa icles eac i i y bo h o nanosa e y and nanomedicine pu poses, because i is p edic i e o he amoun and ype o coa ing ha masks su ace si es, p o id- ing a new biological iden i y o he dispe sed phases [60,64]. Few s udies epo in silico models o he p edic ion o Ze a po en ial based on physicochemical in insic p ope ies [60,65]. One o he mos p omising app oaches ha could cope wi h he challenge o ex insic desc ip o s is an applica ion o a omis- ic simula ions complemen ed wi h coa se-g ained models o ENM and biomolecules (see he dedica ed sec ions below). In such mul iscale app oach, he coa se-g ained models (nano and mic oscale) a e pa ame e ized and possibly alida ed by he da a ob ained om mo e de ailed models a smalle scales (a omis ic and quan um chemical). In his con ex , no el desc ip o s can also be de i ed om he s a is ics o adso bed molecules a e an analysis o he biomolecula co ona [66,67]. Mode n machine-lea ning me hods Machine lea ning (ML) is a b anch o a ificial in elligence (AI) ha in ol es aining compu e p og ams o make p edic ions o ake ac ions based on da a. In ML, algo i hms a e designed o lea n om da a, ins ead o being explici ly p og ammed o pe - o m specific asks. The idea is o p o ide he compu e wi h a la ge amoun o da a, and hen use his da a o each he compu e how o iden i y pa e ns, make p edic ions, o classi y new da a. A i s co e, ML includes ou basic no ions: algo i hms, models, da a, and aining. ML algo i hms a e designed o lea n om da a and make p edic ions o ake ac ions based on ha da a. The algo- i hms gene a e models, which a e ep esen a ions o he pa e ns and ela ionships in he da a. The quali y o he model depends on he quali y and quan i y o he da a used o ain i , as well as he algo i hm’s abili y o lea n om ha da a. The e o e, he p ocess o aining an ML model in ol es eeding i wi h labeled da a, measu ing i s pe o mance, and efining he model un il i achie es sa is ac o y accu acy on new, unseen da a. The a ionale o using ML app oaches o nanosa e y assess- men is based on he need o e ficien ly p ocess, analyze, and ex ac meaning ul in o ma ion om possibly as amoun s o da a gene a ed om bo h compu a ional and expe imen s. ML algo i hms can lea n complex ela ionships and pa e ns om hose da a se s, enabling esea che s o make p edic ions, op i- mize ma e ial p ope ies, and iden i y no el ma e ials wi h desi ed (e.g. less oxic) cha ac e is ics. The ad an ages o using ML - as opposed o mo e adi ional app oaches - include: Accele a ed ma e ials disco e y and op imiza ion: ML algo- i hms can quickly p ocess la ge amoun s o da a and iden i y po en ial new ma e ial candida es o modifica ions o u he in es iga ion. Reduced expe imen al and compu a ional cos s: By p edic ing ma e ial p ope ies and hidden pa e n iden ifica ion, ML can help educe he numbe o expe imen s and simula ions equi ed in he de elopmen p ocess. Enhanced unde s anding o complex ma e ial sys ems: ML can cap u e non-linea ela ionships and in ica e pa e ns in da a, leading o be e insigh s in o he unde lying physics and chemis y o ma e ials. Howe e , he e a e also challenges and limi a ions associa ed wi h such ML app oaches: Da a quali y and a ailabili y: ML algo i hms ely on high-quali y and abundan da a, which can be a limi ing ac o in ma e ials science, whe e da a may be sca ce, noisy o he e ogeneous. In e p e abili y and explainabili y: ML models can be com- plex and di ficul o in e p e , making i challenging o unde - s and he unde lying easons o hei p edic ions and build us in hei ou comes. O e fi ing and gene aliza ion: ML models may be p one o o e fi ing, namely hey pe o m well on he aining da a bu poo ly on unseen da a, hus educing p edic ing accu acy. ML echniques ha e been applied o a wide ange o applica- ions and fields including nanoin o ma ics [68]. In such a con- RESEARCH: Re iew Ma e ials Today d Volume 67 d July/Augus 2023 RESEARCH 351 ex , ML me hods ha e he po en ial o significan ly impac he design, cha ac e iza ion, and sa e y assessmen o ENMs. How- e e , he a ailabili y o high-quali y and abundan da a is s ill an impo an aspec o add ess. Among o he challenges, a pa icula ly impo an aspec asso- cia ed o da a-based modelling in nanoin o ma ics is he p ope handling o highly imbalanced da ase s. Imbalanced da ase s a e cha ac e ized by a skewed class dis ibu ion (e.g., o e 1:100 obse a ions in he mino i y class compa ed o he majo i y class), whe e usually he mino i y (unde ep esen ed) class is he mos in e es ing one o p edic . Fo example, in an unbal- anced da ase , he e could be a majo i y class o nanoma e ials ha a e composed o a single elemen (e.g. gold nanopa icles) and mino i y classes o nanoma e ials ha a e composed o mul- iple o he elemen s. As a esul , when aining a ML o classi y he nanoma e ials he model is likely o be biased owa ds he majo i y class and may no pe o m well on he mino i y class o high in e es . To add ess he issue o class imbalance, a ious da a-le el and algo i hm-le el app oaches ha e been p oposed o e he las dec- ades [69–71]. Da a le el app oaches a e add essing class imbal- ance ia esampling (unde sampling and o e sampling), as well as ia e olu iona y algo i hms o sampling, ac i e lea ning o selec ing he mos app op ia e da a poin s o mo e ecen ly ad e sa ial lea ning algo i hms o new poin s gene a ion and me a-lea ning [72]. Random unde sampling (i.e. emo al o obse a ions om he majo i y class), Nea Miss, Tomek links (i.e. emo al o bounda y obse a ions) a e examples o unde - sampling (o downsampling) algo i hms. The disad an age o he la e me hods is he loss o use ul in o ma ion as well as pos- sible inc ease o he da a bias. O e sampling app oaches include andom o e sampling (mul iplica ion o obse a ions om he mino i y class), SMOTE (finding nea es neighbo s o he mino i y class obse a ions and adding poin s on he line joining he poin and he nea es neighbo ) [73]. A e iew o SMOTE a ian s can be ound in Re . [74] SMOTE ex ensions o handle mul iclass and mul ilabel (MLSMOTE) classifica ion and eg ession (SMOTER) ha e been p oposed [75], and, mo e ecen ly, DeepSMOTE [76] and G aphS- MOTE [77]. O e sampling has he ad an age o e aining all he in o ma ion and usually pe o ms be e han unde sampling. Howe e , o e sampling may inc ease he p obabili y o o e fi ing. Ano he impo an aspec o be conside ed is ha accu acy is no an app op ia e me ic o assessing model pe o mance on imbalanced da ase s. Algo i hm-le el app oaches aim a modi y- ing he loss me ic, assigning di e en cos s o penalize e o s in each class (cos -sensi i e aining) o in oducing new algo i hms which can inhe en ly deal wi h imbalanced da a. Ins ead o accu- acy, he ecommended me ics a e con usion ma ix, p ecision and ecall, F1 sco e, Kappa, A ea unde cu e (AUC) (see also Re . [74]). Ensemble algo i hms using bagging and boos ing (e.g., ee ensembles as Random Fo es ) a e known o be mo e obus in imbalanced se ings. Recen li e a u e add esses han- dling imbalanced da a (also known as long ail lea ning) by deep lea ning me hods [78,79]. Imbalanced da ase s a e ypical in high h oughpu sc eening [80–82] and chemogenomics [83]. ML algo i hms can be b oadly ca ego ized in o supe ised and unsupe ised lea ning. We begin wi h a discussion o supe ised lea ning in he ollowing subsec ion. Supe ised lea ning Supe ised lea ning wo ks on labelled da a, wi h he goal o app oxima ing a unc ion ha maps inpu o labelled da a. A p o o ypical example would be he abili y o link a se o ele an in insic and ex insic ad anced desc ip o s o a numbe o ENMs o hei obse ed (eco-) oxicological endpoin s, e.g. he su i al and/o ep oduc ion a e o a chosen o ganisms. Supe - ised lea ning echniques include a la ge a ie y o algo i hms and me hods like Decision T ees, Random Fo es , Suppo Vec o Machines, a ious classifie s like k-Nea es Neighbo s, Neu al Ne wo ks and Ins ance-Based Lea ning me hods (see Fig. 3, le panel) [84,85]. In one o he ea lies applica ions o ML me hods in manu ac- u ed nanopa icles, Fou ches e al. used Suppo Vec o Machine-based classifica ion and kNN-based eg ession o gene - a e Quan i a i e Nanos uc u e–Ac i i y Rela ionship (QNAR) models o p edic biological ac i i y p ofiles o no el nanoma e- ials [86]. La e , Puzyn e al. [52] p esen ed a me hod o quickly es he po en ial oxici y o enginee ed nanopa icles. They applied a mul iple eg ession me hod combined wi h a Gene ic Algo i hm (GA-MLR) o c ea e a model ha desc ibed he cy o- oxici y o 17 di e en ypes o me al oxide nanopa icles o bac- e ia Esche ichia coli [52]. Ge nand and Casman pe o med a eg ession- ee-based me a-analysis on oden pulmona y oxic- i y exposed o uncoa ed, non- unc ionalized ca bon nano ubes. They epo ed he applica ion o Reg ession T ee models, Ran- dom Fo es models, and a andom- o es -based dose– esponse model [87]. Winkle e al. used no el spa se ML me hods o model he biological e ec s o nanopa icles wi h a ious com- posi ions, including i on oxide nanopa icles and gold nanopa - icles. They employed Bayesian neu al ne wo ks using bo h linea and nonlinea ML me hods [88]. E olu iona y app oaches ha e also been explo ed. Le and Winkle [89], o example, e iewed he use o a ificial e olu- iona y me hods o he iden ifica ion and op imiza ion o no el ma e ials. They epo uses o gene ic algo i hms o in es iga e he p ope ies o bime allic co e–shell and i anium dioxide nanopa icles [90,91]. Ma inez e al. p esen ed decision ee models based on e olu iona y algo i hms ha classified nanopa icle agg ega es in o mo phological classes [92]. kNN algo i hms ha e also been used o model he oxicological p op- e ies o nanoma e ials. Wang e al. used a kNN algo i hm o de elop QNAR models o biological ac i i y p ofiles like cellula up ake in a ious human cells and he abili y o induce oxida i e s ess [93]. Ko alishyn e al. used kNN, andom o es , and neu al ne wo k me hods o gene a e models o he analysis o eco/ ox- icological and physicochemical p ope ies o me al and me al oxide nanopa icles [94]. Va ious Neu al Ne wo k a chi ec u es ha e also been in es i- ga ed in ela ion o p edic i e nanoin o ma ics. Gomez- Bomba elli e al. ained a Deep Neu al Ne wo k o au oma ically gene a e no el chemical s uc u es and demons a ed hei me hod on small d ug-like molecules [95]. Ha aminia e al. used RESEARCH: Re iew RESEARCH Ma e ials Today d Volume 67 d July/Augus 2023 352 e ized by he nanopa icle’s diame e and in e ac ing wi h o he beads acco ding o analy ical equa ions desc ibing he in e ac ion po en ial (e.g. fi ed o calcula ed exp ession by means o classical Molecula Dynamics o a nanopa icle pai , see Fig. 4, panel C). Applying his coa se-g aining p ocedu e makes i possible o de e mine new molecula desc ip o s uling pa icle agg ega ion ha could be ed in o QSAR models [59]. Addi ional challenges aced by de eloping gene al compu a ional and heo e ical mod- elling o unde s anding he nanosa e y o ENMs s em om he ole o he en i onmen in de e mining he obse ed oxici y. Once he ENM en e s a li ing o ganism, i ge s in con ac wi h he biological molecules, in pa icula p o eins and lipids. Nanopa icle-p o eins and nanopa icle-lipids in e ac ion could in heo y be in es iga ed using B ownian Dynamics simula- ions, which can un e en on a 16 co e wo ks a ion, p o ided ha all pai in e ac ions a e known; in p ac ice, his has no ye been done because he calcula ion o he ee ene gy p ofiles o each pai by all-a om MD is oo compu a ionally demanding. Anyway, his could be an in e es ing a emp o b idge he gap be ween he molecula simula ions scale and he expe imen al scale. The oldes app oach o compu a ionally de e mining ma e ial p ope ies, i.e. he con inuum mechanics pionee ed in he 19 h cen u y by Cauchy, is in ac mos sui ed o sc eening pu poses, since i combines modes compu a ional cos s o ealis ic sys em sizes wi h a ew e ec i e sc eening pa ame e s. This sc eening idea is a he basis o con inuum Sel -Consis en Field Theo y (SCFT), which was de eloped o desc ibe phase beha io and phase sepa- a ion dynamics in block copolyme s ( ep esen ed as flexible chains) based on an implici molecula ep esen a ion [133]. Rigid objec s like nanopa icles ha e also been inco po a ed in o SCFT, p ima ily o he pu pose o modelling polyme nanocomposi es [134], and we e e o ea ly pape s o de ails abou he di e en app oaches [135–137]. While hese field- based me hods possess a clea ad an age o e ficiency o e pa icle-based me hods like AAMD and CGMD, which s ems om he choice o deal wi h ensembles a he han indi idual chains, and SCFT in e ac ions a e o he desi ed many-body ype by defini ion, his is o se by he se ious disad an age o no being able o ep esen specific in e ac ions a he molecula le el and ha ing no access o con o ma ional de ail. In addi ion, he commonly used excluded olume in e ac ions in SCFT do no allow o phase ansi ions ha can play a ole in memb ane binding p ocesses. Only ecen ly, hyb id pa icle-field (hPF) app oaches such as hPF-MD [138] and single chain in mean field (SCMF)[139] ha e in oduced he abili y o combine pa icle- based (a omis ic o segmen al) molecula de ail wi h he e fi- ciency and mul i-body na u e o Hamil onians om con inuum heo y like SCFT. Phase ansi ions and/o coexis ence ha e also been added ecen ly [140]. Un il hese hyb id me hods o e a alida ed solu ion o he need o combine e ficiency wi h speci- fici y and molecula de ail, we conclude ha SCFT is use ul o he in es iga ion o gene al phenomena, bu no sui ed o he ex ac ion o ex insic ma e ial desc ip o s. A popula and e y e ficien ep esen a ion o lipid memb anes is ha o a hin elas ic shee wi hou any molecula de ail. Shape, dynamics and esponses o de o ma ion a e dic a ed by a con in- uum Hel ich ee ene gy ha depends on a ew collec i e p ope - ies like bending igidi y and la e al memb ane ension, which can be di ec ly ela ed o specific memb ane composi ions ia pa icle-based simula ion. An ex ended Hel ich model de eloped la e p o ided s aigh o wa d condi ions o nanopa icle up ake, i.e. he balance o ene gy needed o s e ch and bend he mem- b ane a ound he nanopa icle and he ene ge ic gain o nanopa - icle binding. The la e is p o ided by he adhesion ene gy densi y o (coa ed) nanopa icles in he con ac egion and, as was la e ound, also in nea -con ac egions. I should be no ed ha ex ac ing adhesion ene gy densi y o eal ma e ials om mo e de ailed desc ip ions is, un o una ely, a a om simple ask [141]. Ea ly on, Dese no e al. used he con inuum model o show ha a ensionless memb ane can only adop wo s a es: an unbound s a e whe e he memb ane is fla , o a s a e whe e he nanopa icle is ully w apped by he memb ane [142,143]. Accoun ing o ac o s ha we e missing in his o iginal Hel ich-based analysis, such as he memb ane hickness and in e ac ion ange, Raa z e al. and Spangle e al. ound ha also pa ially w apped cases could be s able [144,145]. While p o id- ing impo an ene ge ic insigh , and, he e o e, being o po en ial use o es ic ed bu quick sc eening, he lack o lipid de ail al eady se iously hampe s he use o such me hods o ex ac ing ex insic ma e ial desc ip o s. Ano he disad an age is ha he na u e o he memb ane de o ma ion upon nanopa icle binding is no an ou come bu equi ed a p io i, in oducing a isk o o e - looking al e na i e binding mechanisms. The his o ical solu ion o his issue is o employ highly coa se-g ained pa icle-based models wi h implici sol en o s udying gene ic memb ane dynamics and nanopa icle-memb ane in e ac ions. Using such a model, he w apping cha ac e is ics o nanopa icles up o 40 nm was conside ed, i.e. he en i e ange o he mechanism is expec ed o swi ch, and a discon inuous ansi ion om pa ial o ull w apping was p edic ed a ound 10 15 nm nanopa icles [145,146]. Since hese models lack sol en , which is known o modula e he ( ee) ene gy landscape, and hey also lack he eso- lu ion o dis inguish be ween di e en lipids, hey do no ep e- sen a decisi e s ep o wa d in he sea ch o accu a e desc ip o s. Summa izing, one may conclude ha hese e ficien molecule- based mesoscopic me hods a e use ul o ex ac ing in o ma ion abou gene al balances and mechanisms o sys ems o ele an size, bu hey we e ne e mean o p o ide ex insic nanopa icle desc ip o s. Jus his, balancing (chemical) in o ma ion and e fi- ciency wi h he aim o e ain he necessa y de ail, is he main pu - pose o ecen de elopemen in sys ema ic coa se-g ained me hodology. Al hough he e a e se e al ways o pe o m sys em- a ic coa se g aining, depending on he cha ac e is ics o he e e - ence a omis ic sys em ha one wan s o ep oduce, hey a e all based on lumping g oups o a oms in o CG pa icles o beads. The mos popula me hod, CG Ma ini, combines 2–4hea y a oms in a single bead. Gene a ing a desc ip ion in e ms o CG beads does no only educe he compu a ional load, bu i also so ens he in e ac ions. As a esul , also he sys em e olu ion is significan ly accele a ed. Me hods based on such ypes o coa se g aining ha e been applied o s udy lipid pa i ioning in gene al, he binding o elas ic nanoshells [147,148], he adhesion o aniso- opic nanopa icles [149,150] and o unc ionalized nanopa icles [151,152]. Also he ole o bending and adhesion in he dis ibu- ion o mul iple nanopa icles inside he memb ane has been in es iga ed, bo h in e ms o a gene ic ep esen a ion in a highly RESEARCH: Re iew Ma e ials Today d Volume 67 d July/Augus 2023 RESEARCH 359 CG me hod [153] and o mo e chemically esol ed CG ep esen- a ions [154–156]. Ye , while hese CGMD s udies ha e ei he been designed o p ope ly ep esen a pa icula expe imen al nanopa - icle o o ob ain insigh in o mo e gene al binding mechanisms, e y ew ha e ocused on he challenge o de eloping a ans e - able ep esen a ion o map om he a omis ic o he coa se- g ained domain. Ye , de e mining such a map ha is alid o all nanopa icles o he same ma e ial is a p e equisi e o he ex ac- ion o ad anced desc ip o s and ends ha enable ex apola ion. One e y ecen example o such a new de elopmen is he special CG nanopa icle ep esen a ion wi hin he amilia Ma - ini CG app oach ha is equi ed o s udying binding and ansloca ion pa hways o ealis ic sil e nanopa icles ac oss sol- a ed lipid ba ie s in he lungs, see Fig. 5. Whe eas he mod- elling communi y has hus a gene ally app oached he undamen als o such la ge-scale phenomena ia implici - sol en con inuum [142,143] o highly coa se-g ained desc ip- ions [145,146,157], a co e–shell CG ep esen a ion was de el- oped ha is ans e able wi h espec o size and enables he simula ion o ele an nanopa icle sizes including sol a ion e ec s, in he size ange whe e in e es ing swi ching in binding beha io is expec ed [158]. The de elopmen o his ans e able map was based on ma ching po en ials o mean o ce (PMFs) o sil e nanopa icles ob ained using all-a om molecula dynamics [120]. The sys ema ic de elopmen o ans e able CG ep esen- a ions o he o he ma e ials, such as silica and i anium diox- ide ha we e also conside ed in a omis ic s udies, is a u u e desi e. As he de e mina ion o binding ee ene gies o CGMD NP by s anda d me hods like umb ella sampling ge s p ohibi i e wi h inc easing size, s ing me hods could be employed as an al e na i e [159]. A key equi emen in nanosa e y assessmen is how o include sys ema ically a de ailed molecula desc ip ion o ENM-p o ein in e ac ions. In biological en i onmen s, p o eins o ganize on ENM su aces o ming he so-called nanopa icle p o ein co ona (NPC) s uc u es which play a cen al ole in biological in e ac- ions and nano oxici y [160–162]. The NPC o ma ion a ound a a ie y o nanopa icles was e idenced and cha ac e ized in e ms o i s biochemical composi ion by se e al expe imen al s udies [163,164]. Howe e , i s e ec s on biological in e ac ions and implica ions o nanosa e y conside a ions emain la gely unknown [163,164–169]. Simila ly as abo e, a main esea ch challenge is how o de elop e ficien ye accu a e compu a ional me hods and ools ha can b idge he gap be ween a de ailed, molecula -le el desc ip ion o ENMs in e ac ing wi h sol en s and biomolecules such as p o eins a an a omis ic le el, and he much la ge scale (i.e., ens o hund eds o nanome e s o mic ome e s) co esponding o biological s uc u es such as NPCs o cellula memb anes [170]. Coa se-g ained compu a- ional me hods o p o ein-co e ed nanopa icles a e o en lim- i ed o modeling en i e p o eins as single pa icles. Such models a e success ul in showing how nanoma e ials ype, size, and shape can lead o di e se p o ein composi ion o he NPC [171,172]. Howe e , de ailed a omis ic aspec s o modeling p o- ein in e ac ions a e equi ed o calcula e o he key expe imen ally- ele an mesoscopic desc ip o s such as he FIG. 5 (a) All-a om MD o nanopa icle/memb ane/wa e sys em, (b) all-a om MD binding ee ene gy o h ee di e en sizes o Ag nanopa icles, (c) all-a om MD binding ee ene gy o silica o h ee nanopa icle sizes (g aphs ep oduced om Re . [120] wi h pe mission om he Royal Socie y o Chemis y). (d) S anda d uni o m CG model and he new co e–shell CG model, ep oduced wi h pe mission om Singhal e al., MDPI Nanoma e ials, 2022[158]. (e) Wi h inc easing hyd ophobici y, he mechanism o di ec inse ion in o he model lung memb ane swi ches o a w apping mechanism.. RESEARCH: Re iew RESEARCH Ma e ials Today d Volume 67 d July/Augus 2023 360 hyd ophobic ac ion o he sol en accessible su ace a ea (SASA H ) (see Fig. 6(a-b-c)). Recen de elopmen s in a omis ic and mul iscale compu a ional me hods allow unique oppo uni- ies o p obe he de ailed molecula mechanisms ha modula e in e ac ions a bio-nano in e aces [173,174,29]. This app oach can be ex ended o he calcula ion o mesoscopic biophysical desc ip o s o an NPC and elies on simplified models allowing u he compu a ional s udies o p o ein in e ac ions wi h ENMs (see Fig. 6(a-b-c)) [163]. This has he po en ial o (i) un eiling he ole o specific p o eins in NPC’s s abili y and biophysical p op- e ies (e.g., hyd ophobic su ace a ea, cha ged pa ches), and (ii) quan i ying he way in which nanopa icle co ona p ope ies and p o ein–p o ein in e ac ions in he co ona a e modula ed o di e en nanopa icle ypes. Fi s , all-a om molecula dynam- ics (MD) simula ions o key plasma p o eins (e.g., human se um albumin, fib inogen, immunoglobulin gamma-1 chain-C, com- plemen C3, and apolipop o ein A1) can be used o s udy adso p- ion on ypical nanopa icle su aces (e.g., i anium dioxide o silica). Fo bina y p o ein–p o ein in e ac ions (e.g., only wo in e ac ing p o eins) i is possible o pe o m exhaus i e a omis- ic MD simula ions, bo h in he icini y o nanopa icle su aces and in bulk o compa e di ec ly he esul s and in e he influ- ence o he p esence o specific nanopa icles on he dynamic and he modynamics aspec s o p o ein–p o ein in e ac ions. Fig. 6(c)) illus a es he possibili y o iden i y esidues c ucial o p o ein-nanopa icle in e ac ions in a specific sys em (he e, human se um albumin- i anium dioxide). In he second s age, he molecula mechanisms o p o ein-nanopa icle in e ac ions a e p obed by looking a he dynamic and s uc u al p o eins o se e al p o eins (and possibly lipids) in he c owded en i on- men o nanopa icle co onas, using also molecula docking sim- ula ions and, depending on sys ems size, coa se-g ained simula ions o mix u es o mul iple p o eins [175,176] ha can in es iga e he o ma ion o he p o ein laye on he nanopa i- cle su ace, as illus a ed in Fig. 6(d-e- ). P elimina y s udies on mul i-p o ein docking on nanopa icles, sugges ha knowledge o p o ein composi ion and con o ma ions (e.g., efined om MD simula ions) can be used o es ima e he o e all biophysical p ope ies o NPCs, such as he hyd ophobic ac ion o hei sol en -accessible su ace a ea, and su ace cha ge dis ibu ions [177]. Ou s anding challenges in modeling he in e ac ions o biological molecules in con ac wi h ENMs a e o ex end he capabili y o docking p og ams o include mo e han jus a ew ens o p o eins [178–181], and o include de ailed in o ma ion on he specific co ona molecula composi ion ha is seldom a ailable [170]. Addi ionally, besides p o eins, i is expec ed ha FIG. 6 (a) Schema ic a omis ic model o an NPC. A ”so co ona”laye (dashed line) o loosely bound p o eins su ounds a ”ha d”co ona laye (con inuous black line), p oximal o he ENM’s su ace. E en o sphe ical nanopa icles, he o e all shape and biophysical p ope ies o he NPC su ace will depend on i s composi ion. (b) Compa ing alues o he SASA H o a ious co onas a ound a 4 nm sphe ical silica nanopa icle (dashed) wi h he alues calcula ed o simila p o ein agg ega es wi hou including a nanopa icle (con inuous). (c) A omis ic co ona models allow he iden i ica ion o p o ein esidues ha may play signi ican oles a he nanopa icle-p o ein in e aces. (d-e- ) Building an a omis ic model o an NPC by sequen ial docking o p o ein s uc u es (mucin, p e- equilib a ed using MD) on a sphe ical silica nanopa icle. Mesoscopic desc ip o s such as SASA H can be es ima ed as s a is ical a e ages o e esul s om docking mul iple ep esen a i e s uc u es. RESEARCH: Re iew Ma e ials Today d Volume 67 d July/Augus 2023 RESEARCH 361 u u e s udies will also include (i) o he componen s such as lipids [182] and glycans [183] which play pi o al oles in ENMs up ake and could also be key o he modeled ENM sys ems, as well as (ii) an accu a e desc ip ion o he co esponding su ace unc ionaliza ion [184]. Finally, me allic nanopa icles dese e a special men ion as hey a e ou inely employed in cance he - apy, whe e hey need o be selec i ely deli e ed o he umo is- sues. Among all me als, gold nanopa icles a e widely used as adiosensi izing agen s because o hei biocompa ibili y and simplici y o syn hesis [185]. Coa ed me allic nanopa icles a e used in ca alysis, sel -assembly, imaging, d ug deli e y, and sens- ing applica ions. Me allic nanopa icles a e e y sensi i e o he local en i onmen because o a phenomenon called “localized su ace plasmon esonance”de i ing om he collec i e oscilla- ion o su ace elec ons. [186] When coa ed wi h a monolaye ligand, he me allic nanopa icles’p ope ies such as me al educ ion and colloidal s abili y can be adjus ed o he desi ed applica ion [187]. A omis ic and mesoscale simula ions allowed an unde s anding o he a ypical dis ibu ion o mul iple ligands on gold and sil e nanopa icles obse ed in he expe imen [188], as well as he adso p ion o biomolecules on gold nanopa - icles o di e en sizes [189]. We epo in Fig. 7 an example o how molecula modelling simula ions (he e all-a om molecula dynamics simula ions) can be used o in es iga e he beha io o me allic nanopa icles, in pa icula , o cap u e adso p ion phenomena o polyme s. In e es ingly in [190], i is clea ly shown ha he design o shape and opology o he su ace in me allic nanopa icles is a a he e ec i e s a egy o con ol he p e e en ial polyme coa ing in some pa icle egions as com- pa ed o o he s. Fu he mo e, using simila simula ions ools, o he wo ks [188] ha e in es iga ed he p ecise pa e ning o coadso bed su ac an s on sil e and gold nanopa icles. Hence, by a ge ing mo e e ec i e con ol on he c ys allog aphic ea- u es o me allic nanopa icles, we can en ision an imp o ed con ol o ENMs coa ing hus also significan ly a ec ing hei oxicological p ope ies. Discussion: Challenges and pe spec i es On one hand, om he abo e o e iew, i clea ly eme ges ha he accu acy in p edic ing possible haza ds o ENMs c i ically elies upon he abili y o use ea u es beyond wha can be accessed in ypical expe imen al es s and cha ac e iza ion. Impo an ly, hose ea u es depend on in insic and ex insic p ope ies aiming a desc ibing bo h ma e ials and biological en i onmen s. None heless, when i comes o he di ec link o ENMs o hei expec ed oxici y endpoin s, i is ai o say ha he cu en s a- us o he de elopmen o he ha dwa e and algo i hms does no allow a b u e- o ce assessmen o nanosa e y. A mo e iable app oach is expec ed o be he calcula ion o in insic and ex in- sic ad anced desc ip o s using a ple ho a o me hodologies mos ly de eloped and used in o he fields (e.g. ma e ials model- ing and biochemis y) o ex ac inpu ea u es o da a-based o s a is ical models (e.g. QSAR, ML algo i hms) o finally link hem o he oxici y endpoin s. FIG. 7 Uppe panel: MD snapsho s o sel -assembly simula ions o 60 PLGAs and he Au nanopa icle in aqueous solu ion a 0, 0.5, 50, and 100 ns, ep oduced wi h pe mission om Cappabianca e al., ACS Omega, 2022[190]; Bo om panel: Equilib ium s uc u es ob ained by mesoscale simula ions o sel -assembly o bina y mix u es o su ac an s wi h a ying leng h di e ence o bulkiness di e ence on a sphe ical nanopa icle. Da k ( ed) beads and ligh (yellow) beads ep esen head g oups o he wo species o su ac an s, om Re . [188]. See ex o mo e de ails.. RESEARCH: Re iew RESEARCH Ma e ials Today d Volume 67 d July/Augus 2023 362 Howe e , e en he la e app oach comes wi h o midable challenges, mos ly associa ed wi h he size and complexi y o ENMs o p ac ical use. On one hand, ENMs o expe imen al in e es may ha e dimensions o de s o magni ude la ge han hei compu a ionally a o dable coun e pa s. On he o he hand, p ecise composi ions o pa icles and hei coa ing a e o en known wi h li le de ail le el: This calls o a c ucial e o o he ele an scien ific communi y in u u e expe imen al wo ks whe e, in addi ion o he aluable measu emen o oxico- logical endpoin s, a mo e comp ehensi e cha ac e iza ion beyond nominal alues o ENMs is eques ed. Fu he mo e, cu en ly, an in e es ing (and pe haps necessa y) app oach seems o be he hyb idiza ion o pu e physics-based mod- els wi h dispa a e da a sou ces. In pa icula , due o a p ac ically unlimi ed numbe o di e en ENMs wi h g ea chemical and geo- me ical a ie y, a uly ex ended adop ion o ad anced desc ip o s in da a-based models o nanoin o ma ics looks inconcei able wi hou le e aging high-fideli y mul iscale modeling da a om bo h li e a u e and c ude o analy ical (ye compu a ionally e fi- cien ) app oxima ions models. As a ep esen a i e example, biased classical molecula dynamics simula ions can ce ainly be used o accu a ely compu e he Po en ial o Mean Fo ce be ween nanopa - icle pai s. A he same ime, classical app oaches such as he heo y o De jaguin-Landau-Ve wey-O e beek (DLVO) canno be dis- ca ded and e o s should be de o ed o finding new app oaches capable o o ches a ing and in eg a ing such mul i-fideli y and mul i-sou ce da a. Ano he example o a simila syne gy has been desc ibed abo e in he manusc ip and i has o do wi h CGMD based simula ions o cell memb anes and he co esponding con- inuum Hel ich ee ene gy models. One possibili y would be he adop ion o desc ip o s om c ude app oxima ion models, cha ac e ized by a lowe fideli y le el and a ailable li e a u e da a as a subse o ea u es o ML models whe e physics-based model esul s a e used as aining se s. Such an app oach has been p o en success ul in signifi- can ly imp o ing ML model p edic ions in he p esence o a small o incomple e aining da ase [11,193]. We hus en ision a mo e comp ehensi e and mul i-laye ed app oach whe e de ailed physics-based models (fi s laye ), li e a u e/expe imen da a (second laye ), and c ude app oxima ion models ( hi d laye ) a e syne ge ically managed o he es ima e o ele an desc ip o s by means o su oga e (s a is ical) and ML models (see Fig. 1, bo om panel). In his espec , u he esea ch is eques ed o in es iga e o wha ex en he small a iance o high-fideli y da a om physics-based model p edic ions com- bined wi h he high- a iance (and low-fideli y) o o he sou ces can deli e desc ip o s p edic ions wi h high/medium fideli y and a iance ha a e sui able o QSAR/QSPR models. I is also wo h s essing ha , beyond he me e use ulness o ad anced desc ip o s o da a-based models a ge ing nanosa e y assessmen , u he imp o ed physics-based models desc ibing phenomena om he elec onic up o he mesoscopic le el can o e he unique oppo uni y o gaining insigh s a he nano- le el, which a e ha dly accessible by expe imen al echniques and he e o e emain c i ical o i) un eiling basic mechanisms behind he possible haza dous cha ac e o ENMs; ii) possibly complemen in o ma ion om less de ailed and less demanding models. Fo all he abo e easons, we expec and hope ha he p o- g ess o high-pe o mance compu ing powe combined wi h ad anced ools o accele a ion o a omis ic simula ions [194] can soon calcula e ad anced desc ip o s mo e compe i i ely as compa ed o expe imen s in e ms o bo h he amoun and qual- i y o gene a ed da a. Ano he in e es ing aspec o highligh is ela ed o he possi- ble exploi a ion o he la ge body o knowledge a ailable in he con ex o mode n compu a ional app oaches o in es iga e nanopa icles in o he echnological fields such as ca alysis and ma e ials science [195–196,77,197–200]. Specifically, we e e o: 1. High- h oughpu sc eening o nanopa icles in d ug deli e y [201] 2. Ba coded nanopa icles o high h oughpu in i o disco e y o a ge ed he apeu ics [202]. 3. Compu a ional high- h oughpu sc eening o alloy nanoclus- e s o elec oca aly ic hyd ogen e olu ion [203] I would be desi able ha he Sa e and Sus ainabili y-by- design s a egies in oxicology could ake ad an age o his knowledge and ools, combining e ficien ly biology and medi- cine wi h physics and chemis y. Fu he mo e, an e en la ge amoun o da a on nanosa e y o ENMs will likely be gene a ed in he nea u u e. I is he e o e o inc easing impo ance o comply wi h he FAIR p inciples, so ha me ada a and da a can be e ficien ly eused in da a-based models o p edic ing he haza d o ENMs. Specifically, in his espec , he ole o da abases o collec ing and s o ing a la ge amoun o cu a ed and well s uc u ed da a is likely o play a majo ole soon. As such, we discuss one p o o ypical case s udy in he ollowing subsec ion. Finally, as a long- e m goal, we expec ha nanoin o ma ics models based on ad anced desc ip o s could be in eg a ed wi h AOPs, o be e assess he po en ial exposu e h oughou he en i e li e o he nanopa icles. In his espec , mode n g ouping s a egies aking in o accoun he mode o ac ions and de eloped based on ML echniques p ocessing da a om omics s udies appea pa icula ly p omising and he e o e i will be specifically discussed below in a dedica ed subsec ion. Da abases: The eNanoMappe case s udy S o age and o ganiza ion as well as he o ganiza ion o cu a ed da a om dispa a e sou ces wi hin da abases appea c i ical o nanosa e y assessmen . To his end, below we e iew a p o o yp- ical case s udy. The eNanoMappe da abase is an open-sou ce chemical subs ance da a managemen solu ion [204], adop ed by mo e han 20 Eu opean p ojec s and acili a ing he Findable, Accessible, In e ope able and Reusable (FAIR) da a collec ion and euse o he nanosa e y communi y. To p o ide agg ega ed find- abili y, accessibili y, and in e ope abili y ac oss p ojec -specific da abases, he Nanosa e y Da a In e ace (h ps://sea ch.da a. enanomappe .ne ) was c ea ed, and cu en ly ep esen s one o he la ges sea chable nanosa e y da a collec ions [205]. The eNanoMappe is based on da a and so wa e o iginally de eloped o ep esen indus ial chemicals and ela ed expe imen al o cal- cula ed da a. I was one o he fi s chemin o ma ics pla o ms o o e open REST Applica ion P og amming In e ace (API) sup- RESEARCH: Re iew Ma e ials Today d Volume 67 d July/Augus 2023 RESEARCH 363 po ing in eg a ed se ices such as da a, desc ip o calcula ions, and ML [206,207]. A isual ep esen a ion o he eNanoMappe da a model is epo ed in Fig. 8, whe e subs ances a e cha ac e - ized by names and IDs, which can be mul iple, he composi ion e e s o he componen s o he ma e ial (co e, coa ing, chemical s uc u e), each o hem ha ing di e en p ope ies; he same ma e ial can ha e di e en composi ions. A p o ocol consis s o measu emen s o a specific endpoin in gi en condi ions, ela ed p o ocols o m an in es iga ion en i y; finally, di e en subs ances can be g ouped in o an assay en i y when he same p o ocol applies, gi ing an ex emely flexible s uc u e [208]. Wi h he explosi e g ow h o ma e ial da abases, ML ame- wo ks, and hei success in ma e ial modeling, i is c i ical o explo e he link be ween he es ima ed ma e ial p ope ies and expe imen ally measu ed sa e y o unc ional p ope ies. In NanoIn o maTIX [36], bo h expe imen al da a om selec ed use cases and also calcula ed desc ip o s a e s o ed in he eNanoMappe da abase and an e o is de o ed owa ds p o id- ing open sou ce lib a ies o acili a e in eg a ion wi h da a anal- ysis amewo ks and de eloping explo a o y da a analysis me hods. The alida ion o compu a ional models elies on high-quali y expe imen al da a; such da a may no always be comple e, and i is necessa y o iden i y da a gaps and, e en u- ally, o gene a e addi ional da a based on expe imen al and he- o e ical chemis y and on biology. The goal o his compu a ional and heo e ical endea o is he ealiza ion o sa e nanopa icles and nanoma e ials. To ulfil such expec a ions, heo e ical desc ip o s mus be ansla ed in o measu able pa ame e s, and his challenge en ails a deep knowledge o he mode o ac ion o nanopa icles ha lead o ha m ul ou comes. On ano he le el, iden ifica ion and es ima ion o heo e ical d i- e s o oxici y can acili a e he p io i iza ion o expe imen al es s, which, due o he uncon ollable inhomogenei y o any ensemble o nanopa icles, is always needed o a obus isk assessmen o nano-enabled echnology. G ouping app oaches Regula o y p ocesses, which o en ely on in i o es ing, a e ou - paced by he inc easing numbe o ENMs on he ma ke . To cope wi h his si ua ion, he lack o da a and o ensu e he sa e y o new ma e ials, g ouping app oaches eme ge as an in e es ing me hod. Those app oaches a e accep ed wi hin he o e a ching EU chemicals egula ion REACH (EC 1907/2006) and a e com- monly used o conside mo e han one chemical a he same ime [209–211]. Wi hin an es ablished g oup, da a gaps can be filled by ead-ac oss. He e, exis ing da a on a pa icula (eco) ox- icological endpoin linked o one o se e al sou ce chemicals can be employed o es ima e he same p ope y o one o mo e a ge chemical(s). Chemicals can be g ouped on he basis o well-defined physic- ochemical simila i ies, like common unc ional g oups, p ecu - so s, and/o b eakdown p oduc s. Howe e , ENMs pose an addi ional challenge compa ed o chemicals, since he e is a e y limi ed unde s anding o how indi idual physicochemical pa ame e s influence cellula up ake and oxici y. In addi ion, he p ope ies o ENMs can change depending on he su ound- ing medium and o e ime. To es ablish g ouping app oaches o ENMs, i is essen ial o unde s and how he indi idual physico- chemical p ope ies a e linked o oxici y. To suppo a g ouping jus ifica ion, addi ional in o ma ion on a common Mode o Ac ion (MoA) o oxici y mechanism is ad an ageous [209]. The MoA o a subs ance desc ibes he unc ional o physiological FIG. 8 eNanoMappe /Ambi da a model adap ed om Re . [208]. Subs ances a e cha ac e ized by hei composi ion and iden i ied by name and ID, which can be mul iple. Complex ela ions be ween he subs ance componen s can be speci ied. RESEARCH: Re iew RESEARCH Ma e ials Today d Volume 67 d July/Augus 2023 364 changes i causes o a li ing o ganism o cell. One o he d aw- backs is ha oxici y mechanisms a e only pa ially unde s ood, and o plen y o ENMs a ian s, he p ecise MoA emains elu- si e. In his ega d, he po en ial o sys ems biology o con ibu e o he de elopmen o eliable g ouping app oaches o ENMs should no be obli e a ed. Mode n omics-based app oaches ( an- sc ip omics, p o eomics, me abolomics), in combina ion wi h sophis ica ed bioin o ma ics and da a analysis ools, a e e y impo an o cha ac e ize oxici y pa hways and un a el ela- ionships be ween indi idual physicochemical p ope ies and cellula esponses. This knowledge can hen be applied in he con ex o g ouping and ca ego iza ion. Mo eo e , omics app oaches a e o ele ance o he de elopmen o AOPs and o es ablish eliable, comp ehensi e es ing s a egies building on he known MoA [212]. AOPs a e a concep ual cons uc ha in eg a es known in o ma ion om a ious sou ces in a sequen- ial chain o causally linked key e en s ha co e di e en le els o biological o ganiza ion (i.e. cellula , o gan le el) s a ing wi h a molecula ini ia ing e en leading o he final ad e se ou come [213]. The knowledge gained om g ouping app oaches can hen be di ec ly used o SSbD o ENMs. Cu en ly, he e a e se - e al ENM g ouping amewo ks wi h di e en app oaches [214– 216]. Howe e , he e a e only e y ew case s udies o which he amewo ks ha e been applied o ENMs. Recen ly, se e al publica ions ha e aken ad an age o di e - en echniques, including ML app oaches. [217–219] Addi ion- ally, he inco po a ion o omics da ase s o suppo he de elopmen o mo e accu a e g ouping s a egies o ENMs ake in o accoun he mode o ac ion [220–223]. Bioin o ma ics and ML echniques a e hus essen ial when app oaching cellula e ec s comp ehensi ely, pa icula ly in combina ion wi h high- h oughpu echniques like omics. Fig. 9 depic s he ML andom o es app oach used o he g oup- ing o ENMs, wi h his s a egy, he biological ac i i y o ENMs can be p edic ed p o ided ha physicochemical p ope ies a e known. Omics s udies a e a massi e sou ce o da a se s, which comp ehensi ely desc ibe he cellula al e a ions caused by any ea men , impo an ly in his case, by ENMs. Thus, omics me h- ods a e highly use ul o iden i y he MoA o ENMs o be employed in he g ouping app oaches as addi ional biological desc ip o s. So a , mos o he e o s o unde s and he MoA o ENMs ha e been unde aken in he field o ansc ip omics [224,225]. Howe e , p o eomics can be e en mo e in o ma i e since i depic s he cellula al e a ions much close o he pheno- ype han ansc ip omics. The challenge he e is he s anda diza- ion o he me hods, pa icula ly o da a analysis and in e p e a ion [226,227]. Me a-analysis o publicly a ailable p o eome da a a ge ed o specific o gan al e a ions is being ca ied ou o in es iga e he MoA o ENMs wi hin he o gan, based on p o eome al e a ion e i- dence. Rele an da ase s om publicly accessible p o eomics da a- bases such as PRIDE a e iden ified in he fi s s ep. These da a do no necessa ily in ol e only ENM ea men s, bu also o he al e - a ions like disease, cance , and chemical ea men s. A ecen ly de eloped wo kflow by he B R o s anda dized da a analysis FIG. 9 Schema ic ep esen a ion o he andom o es app oach used o g ouping o ENMs. ENMs a e desc ibed by a se o di e en physicochemical p ope ies, and an ac i i y label is assigned o each ENM (based on he ou come o biological assays). ENMs a e hen subjec ed o a andom o es model, in which ENMs a e di ided in o ac i e and passi e ma e ials, based on spli s made on hei physicochemical p ope ies in each ee. Recu si e ea u e elimina ion is used o selec only he mos impo an physicochemical p ope ies o achie ing maximum accu acy. To add ess and a oid a la ge o e i ing bias, he model is alida ed in a lea e-one-ou app oach. This app oach can be used o p edic he biological ac i i y o new ENMs, o which he physicochemical p ope ies a e known. RESEARCH: Re iew Ma e ials Today d Volume 67 d July/Augus 2023 RESEARCH 365 was applied o he da a se s. These esul s a e hen in eg a ed in o p o eomics da a de no o gene a ed om s udies e alua ing he e ec o ENMs in i o. Co ela ions be ween nanoma e ials e ec s and o gan-specific al e a ions can hus be de ec ed.[228] Concluding ema ks Gaining a clea and deep a ionale behind he nanosa e y cha ac- e is ics o ENMs is a mul i ace ed issue s ill posing o midable challenges: nanoma e ials p esen a mo e complex physico- chemical p ope ies han hei mac oscopic coun e pa s, ha ing high su ace eac i i y and he abili y o en e li ing cells, po en- ially causing damage o cells o en i e o ganisms. In his wo k, mos ly ocusing on a compu a ional pe spec i e, we made an e o o e iew and discuss s a e-o - he-a physics- based models o compu ing bo h in insic and ex insic ENMs p ope ies ha a e c ucial o se ing up eliable da a-based mod- els o nanosa e y assessmen . In his spi i , one majo aim o his wo k was he iden ifica ion o he mos c i ical oadblocks owa ds compu e -aided suppo o nanosa e y assessmen . Impo an ly, we ha e iden ified and discussed oppo uni ies in ad ancing he field and in opening esea ch di ec ions, as con e- nien ly summa ized in Tables 1 and 2. We ha e ex ensi ely illus- a ed ecen ly-used me hods o compu ing ad anced desc ip o s and he cu en associa ed challenges mainly leading o low da a a iance despi e he expec ed highe fideli y. Hence, possible sugges ions on hyb idiza ion s a egies o mo ing owa ds models wi h bo h highe da a a iance and fideli y, as well as he inclusion o Ad e se Ou come Pa hways (AOPs) a e en isioned. We s ess ha he epo ed analysis and sugges ed guidelines o u u e eflec ou cu en bes unde s anding o he field a e se e al yea s o discussion among expe s in mul i-disciplina y ye dispa a e ela ed fields. As such, we hope ha his wo k can se e as a s imulus o u u e mul idisciplina y esea ch, and could hus help nuclea e u he b eak h oughs in he compu a- ional nanosa e y assessmen o ENMs. Da a a ailabili y This is a e iew a icle and da a a ailabili y does no apply. Decla a ion o Compe ing In e es The au ho s decla e ha hey ha e no known compe ing financial in e es s o pe sonal ela ionships ha could ha e appea ed o influ- ence he wo k epo ed in his pape . Acknowledgemen s This esea ch has ecei ed unding om he Eu opean Union’s Ho izon 2020 esea ch and inno a ion p og am unde g an ag eemen No 814426 (NanoIn o maTIX p ojec ). TABLE 2 Summa y o ecommenda ions and possible u u e esea ch a enues in he con ex o using ma e ial modeling echniques o compu ing ad anced desc ip o s. Modelling echnique Recommenda ions o u u e esea ch Quan um Compu a ions (DFT and DFTB) Those a e among he mos ime-consuming me hods o ex ac ing ad anced desc ip o s. The mos ecen me hodologies based on machine lea ning algo i hms a e p omising o expand he se o sys ems, bu a en ion should be paid o hei obus ness and he ene ge ic needs associa ed wi h massi e calcula ions. Plu idisciplina physico-chemical app oaches, such as hose widely used in o he echnological fields like ca alysis, should be used on a egula basis, o cap u e oxici y mechanisms on he molecula le el. This may lead o new desc ip o s and inc ease he p edic i e powe . Reac i e A omis ic Simula ions Sui able eac i e o ce fields a e s ill o be de eloped o accu a ely p edic ing he de elopmen o ENMs su ace elec ical cha ges. In his espec , eaxFF [191] o he mo e ecen Machine Lea ning based po en ials [192] a e expec ed o likely play an impo an ole in coping wi h su ficien ly la ge pa icles beyond he capaci y o s anda d DFT and DFTB compu a ions. Classical All-A om Molecula Dynamics (AAMD) Calcula e he adso p ion a fini y o small me aboli es on nanopa icles using enhanced sampling echniques such as me adynamics. Molecula nano-desc ip o s in [124] ha e p o ed e y compu a ionally e ec i e o cope wi h la ge size pa icles, none heless hey a e limi ed o non-me allic pa icles: Addi ional e o should be spen o elax his cons ain . AAMD is also needed o he molecula docking o p o eins o ENM su aces (see below). To speed up u u e calcula ions, an MD da abase o ypical con o ma ions and hei co esponding he modynamic weigh s could be p e-buil o p o eins ha play a majo ole in in e ac ing wi h ENMs (i.e., albumin, mucin, e c.) o inc ease he accu acy o docking-based calcula ions o nano-desc ip o s (e.g., SASA H ). Molecula P o ein Docking Cu en docking so wa e is op imised o p o ein-d ug and p o ein–p o ein in e ac ions (PPIs). Docking app oaches ely on ini ial AAMD s udies (see abo e) o he p o eins used. Fu u e esea ch would benefi om op imiza ion o sampling e ficien ly and accu a ely p o ein-ENM su aces (i.e., ino ganic ma e ials). Fu u e docking p og ams should be able o handle mul iple molecules, including la ge p o eins wi h di e se con o ma ions and in a much la ge numbe han cu en ly possible (i.e., om ens o hund eds o e en housands). One possible app oach o b idge he gap be ween he limi a ions o docking p og ams and he complex and la ge-scale na u e o PPIs and p o ein-ENM in e ac ions is he de elopmen o ad anced da a-d i en and machine lea ning app oaches. Coa se-G ained Molecula Dynamics (CGMD) Es ima ing abso p ion ene gy densi ies by AAMD o p e-sc eening by con inuum Hel ich me hods. Calcula ing CG PMFs o all possible co e–shell combina ions o gene a e a ma ix o mapping ma e ial-specific a omis ic PMFs. When add essing ex insic desc ip o s o ENMs-cell memb ane binding, o e coming he cu en passi e configu a ions, by adding many lipid ypes ende ing he cellula memb anes as well as memb ane p o eins and he cy oskele on. B ownian Dynamics Simula ing nanopa icles in con ac wi h biological molecules o b idge he gap be ween he molecula modelling scale and he expe imen al scale. Mo e e ec i e app oaches a e needed o compu e and use PMFs in case o non- sphe ical o aniso opic pa icles. RESEARCH: Re iew RESEARCH Ma e ials Today d Volume 67 d July/Augus 2023 366 Appendix ALis o physics-based desc ip o s ha can be calcula ed h ough ma e ials modelling echniques Scale Me hod Ad anced desc ip o Uni s Typical alues o a 3 nm TiO 2 nanopa icle No es and limi a ions Quan um - Space scale: 10 11 -10 9 m Time scale: s a ic DFT & DFTB S anda d en halpy o o ma ion eV -9 o 8 Ve y accu a e bu ideal only o small nanopa icles up o 4 nm in diame e . Highly dependen on he basis se and he pseudopo en ial o choice. Absolu e ene gy alues a e only use ul o compa ison wi h o he s uc u es. To al ene gy eV -90000 o 10000 Elec onic ene gy eV 88000 Ene gy o he Highes Occupied Molecula O bi al (HOMO) eV 3.2 Ene gy o he Lowes Unoccupied Molecula O bi al (LUMO) eV 3.1 HOMO–LUMO ene gy gap eV 0–3 Valence band wid h eV 7.3 Conduc ion band wid h eV 2.6 Fe mi le el eV 3.3 Hyd a ion ene gy eV 143 Ve ical ioniza ion po en ial eV 3.4 Ve ical elec on a fini y eV 3 Oxygen acancy o ma ion ene gy eV 3 o 4 Molecula , a omis ic - Space scale: 10 9 - 10 8 m - Time scale: s a ic Quan um mechanics a DFT le el Chemical composi ion (x9) - N.A. Fas o calcula e because he e is no need o expensi e ene gy minimiza ion. No o me allic ENMs. Calculable also o la ge nanopa icles up o 60 nm. Possible use in QSAR and ML models. De ailed desc ip ion o hose quan i ies can be ound in Re s. [122,124] Po en ial ene gy (x9) eV -75 o 18 Topology desc ip o s (x9) - 2.5 o 6 Size desc ip o s (x3) Å,Å 2 ,Å 3 N.A. La ice ene gy desc ip o s (x5) eV, eV/Å, eV/Å 2 , eV/Å 3 -110 o 0 Fo ce field desc ip o s (x27) - N.A. ReaxFF MD Su ace cha ge C/m 2 -0.06 Need a eac i e o ce field de eloped o a simila sys em Pa icle-Memb ane binding ee ene gy eV 0 Agg ega ion ee ene gy kJ/mol 54 Sol en Accessible Su ace A ea (SASA) Å 2 61.27  s 0.68 No e: See Re . [120]. Molecula scale wi h a omis ic esolu ion. Space scale: 10 10 - 10 8 m AAMD and Molecula Docking Hyd ophobic SASA ac ion (SASA H ) % SASA 33.5–54.5 Pola SASA ac ion (SASA P ) % SASA 40–50 No e: es ima ed o 4 nm TiO 2 NPs wi h di e se p o ein composi ions o (con inued on nex page) RESEARCH: Re iew Ma e ials Today d Volume 67 d July/Augus 2023 RESEARCH 367 Re e ences [1] D. As uc, Chemical Re iews 120 (2020) 461–463. [2] A. Gizza o e al., Ad anced unc ional ma e ials 24 (29) (2014) 4584–4594. [3] A. Ca dellini, M. Fasano, E. 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