Evaluation of biological effects, both desired and undesired, caused by Manufactured NanoParticles (MNPs) is of critical importance for nanotechnology. Experimental studies, especially toxicological, are time-consuming, costly, and often impractical, calling for the development of efficient computational approaches capable of predicting biological effects of MNPs. To this end, we have investigated the potential of cheminformatics methods such as Quantitative Structure – Activity Relationship (QSAR) modeling to establish statistically significant relationships between measured biological activity profiles of MNPs and their physical, chemical, and geometrical properties, either measured experimentally or computed from the structure of MNPs. To reflect the context of the study, we termed our approach Quantitative Nanostructure-Activity Relationship (QNAR) modeling. We have employed two representative sets of MNPs studied recently using in vitro cell-based assays: (i) 51 various MNPs with diverse metal cores (PNAS, 2008, 105, pp 7387–7392) and (ii) 109 MNPs with similar core but diverse surface modifiers (Nat. Biotechnol., 2005, 23, pp 1418–1423). We have generated QNAR models using machine learning approaches such as Support Vector Machine (SVM)-based classification and k Nearest Neighbors (kNN)-based regression; their external prediction power was shown to be as high as 73% for classification modeling and R2 of 0.72 for regression modeling. Our results suggest that QNAR models can be employed for: (i) predicting biological activity profiles of novel nanomaterials, and (ii) prioritizing the design and manufacturing of nanomaterials towards better and safer products.

To engineer gene vectors that target striated muscles after systemic delivery, we constructed a random library of adeno-associated virus (AAV) by shuffling the capsid genes of AAV serotypes 1 to 9, and screened for muscle-targeting capsids by direct in vivo panning after tail vein injection in mice. After 2 rounds of in vivo selection, a capsid gene named M41 was retrieved mainly based on its high frequency in the muscle and low frequency in the liver. Structural analyses revealed that the AAVM41 capsid is a recombinant of AAV1, 6, 7, and 8 with a mosaic capsid surface and a conserved capsid interior. AAVM41 was then subjected to a sideby-side comparison to AAV9, the most robust AAV for systemic heart and muscle gene delivery; to AAV6, a parental AAV with strong muscle tropism. After i.v. delivery of reporter genes, AAVM41 was found more efficient than AAV6 in the heart and muscle, and was similar to AAV9 in the heart but weaker in the muscle. In fact, the myocardium showed the highest gene expression among all tissues tested in mice and hamsters after systemic AAVM41 delivery. However, gene transfer in non-muscle tissues, mainly the liver, was dramatically reduced. AAVM41 was further tested in a genetic cardiomyopathy hamster model and achieved efficient long-term ␦-sarcoglycan gene expression and rescue of cardiac functions. Thus, direct in vivo panning of capsid libraries is a simple tool for the de-targeting and retargeting of viral vector tissue tropisms facilitated by acquisition of desirable sequences and properties.

Evaluation of desired and undesired, biological effects of Manufactured NanoParticles (MNPs) is of critical importance for the future of nanotechnology. Experimental studies, especially toxicological, are time-consuming and costly, calling for the development of efficient computational tools capable of predicting biological events caused by MNPs from their structure and physical chemical properties. This mini-review assesses the potential of modern cheminformatics methods such as Quantitative Structure - Activity Relationship modeling to develop statistically significant and externally predictive models that can accurately forecast biological effects of MNPs from the knowledge of their physical, chemical, and geometrical properties. We discuss major approaches for model building and validation using both experimental and computed properties of nanomaterials. We consider two different categories of MNP datasets: (i) those comprising MNPs with diverse metal cores and organic decorations, for which experimentally measured properties can be used as particle's descriptors, and (ii) those involving MNPs possessing the same core (e.g., carbon nanotubes), but different surface-modifying organic molecules, for which computational descriptors can be calculated for a single representative of the decorative molecule. We illustrate those concepts with three case studies for which we successfully built and validated predictive models. In summary, this mini-review demonstrates that, analogous to conventional applications of QSAR modeling for the analysis of datasets of bioactive organic molecules, its application to modeling MNPs that we term Quantitative Nanostructure Activity Relationship (QNAR) modeling can be useful for (i) predicting activity profiles of novel MNPs solely from their representative descriptors and (ii) designing and manufacturing safer nanomaterials with desired properties.

Growing experimental evidences suggest the existence of direct relationships between the surface chemistry of nanomaterials and their biological effects. Herein, we have employed computational approaches to design a set of biologically active Carbon Nanotubes (CNTs) with controlled protein binding and cytotoxicity. Quantitative Structure-Activity Relationships (QSAR) models were built and validated using a dataset of 83 surface-modified CNTs. A subset of a combinatorial virtual library of 240,000 ligands potentially attachable to CNTs was selected to include molecules that were within the chemical similarity threshold with respect to the modeling set compounds. QSAR models were then employed to virtually screen this subset and prioritize CNTs for chemical synthesis and biological evaluation. Ten putatively active and ten putatively inactive CNTs decorated with the ligands prioritized by virtual screening for either protein binding or cytotoxicity assays were synthesized and tested. We found that all 10 putatively inactive and 7 out of 10 putatively active CNTs were confirmed in the protein binding assay, whereas all 10 putatively inactive and 6 out of 10 putatively active CNTs were confirmed in the cytotoxicity assay. This proof-of-concept study shows that computational models can be employed to guide the design of surface-modified nanomaterials with the desired biological and safety profiles.

Objective To iteratively design a prototype of a computerized clinical knowledge summarization (CKS) tool aimed at helping clinicians finding answers to their clinical questions; and to conduct a formative assessment of the usability, usefulness, efficiency, and impact of the CKS prototype on physicians’ perceived decision quality compared with standard search of UpToDate and PubMed. Materials and methods Mixed-methods observations of the interactions of 10 physicians with the CKS prototype vs. standard search in an effort to solve clinical problems posed as case vignettes. Results The CKS tool automatically summarizes patient-specific and actionable clinical recommendations from PubMed (high quality randomized controlled trials and systematic reviews) and UpToDate. Two thirds of the study participants completed 15 out of 17 usability tasks. The median time to task completion was less than 10 s for 12 of the 17 tasks. The difference in search time between the CKS and standard search was not significant (median = 4.9 vs. 4.5 min). Physician’s perceived decision quality was significantly higher with the CKS than with manual search (mean = 16.6 vs. 14.4; p = 0.036). Conclusions The CKS prototype was well-accepted by physicians both in terms of usability and usefulness. Physicians perceived better decision quality with the CKS prototype compared to standard search of PubMed and UpToDate within a similar search time. Due to the formative nature of this study and a small sample size, conclusions regarding efficiency and efficacy are exploratory.