Evaluation of A Prototype of Computerized Health Knowledge Summaries

Physicians raise two questions in every three patients they see and around 50% of those questions are not even pursued due to various reasons. These unanswered questions represent huge knowledge gap and could result in less than desirable treatment outcomes. The situation becomes even worse with the emergence of internet technologies which brought explosively increasing information and knowledge into everybody's lives. To make medical information more readily available and to facilitate physicians' decision making process, we designed and developed a medical knowledge summary system that automatically extract and synthesize relevant medical evidence from major resources including UpToDate and PubMed. We performed a pilot usability study to evaluate the effectiveness of the system and used the feedback from physicians to further the development effort. Physicians in general found our system intuitive to use and information delivered very valuable in filling in their knowledge gaps.Master of Science in Information Scienc

Quantitative structure-toxicity relationship modeling of organic compounds and nanoparticles

Safety issues are considered the single largest reason for today’s drug development failures. It is both costly and time-consuming for toxicological evaluation of materials. This dissertation focuses on computational modeling of specific toxicityrelated endpoints against chemical compounds and nanoparticles. We concentrate on the application of cheminformatic and QSAR approaches in predicting the toxicity profile for small molecules as well as nanoparticles. Extensive efforts have been made in terms of data collection, data curation, QSAR modeling and virtual screening of external libraries for biologically benign molecules or nanoparticles. Firstly, QSAR analysis has been applied to a group of organic molecules to predict their skin sensitization toxicities. Combinatorial QSAR analysis was utilized to boost the final model performance. 5-fold external cross-validation and y-randomization processes were also applied to validate the robustness of the models. The final models achieved prediction accuracy as high as 83% (for both kNN and RF models) after the implementation of applicability domain. Secondly, we illustrated successful application of QSAR in modeling nanoparticles with two case studies. In both cases, the object datasets consist of nanoparticles with same core structure yet different surface molecular modifiers. In the first study, computational models were developed for cellular uptake property of a series of nanoparticles possessing same core structure (cross-linked iron oxide) with different surface functional groups. Regression models were successfully developed with R02 as high as 0.77 with kNN method after the implementation of applicability domain. Descriptor analysis suggests that the hydrophobicity of the surface molecule may have significant impact on the cellular uptake of iron oxides by pancreatic cancer cells. The second study takes this concept a step further. Besides building statistically significant computational models for predicting the protein binding and acute toxicity properties of a series of carbon nanotubes, an external chemical library consisting of 240,000 molecules were virtually screened in seeking for biologically benign nanoparticles. Moreover, the virtual hit list resulting from the virtual screening exercise was shared with our collaborators for experimental testing. The final results confirm the high prediction accuracy (80% for acute toxicity and 85% for carbonic anhydrase binding endpoint) of the established models. This is also the first-ever study in the area of nanotoxicity to successfully utilizing computational models for prioritizing nanoparticles for experimental testing

Formative evaluation of a patient-specific clinical knowledge summarization tool

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

Quantitative Nanostructure−Activity Relationship Modeling

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

Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles

<p>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 relationship (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 10 putatively inactive CNTs decorated with the ligands prioritized by virtual screening for either protein-binding or cytotoxicity assay were synthesized and tested. We found that all 10 putatively inactive and 7 of 10 putatively active CNTs were confirmed in the protein-binding assay, whereas all 10 putatively inactive and 6 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.</p