Toxicity prediction using multi-disciplinary data integration and novel computational approaches

Current predictive tools used for human health assessment of potential chemical hazards rely primarily on either chemical structural information (i.e., cheminformatics) or bioassay data (i.e., bioinformatics). Emerging data sources such as chemical libraries, high throughput assays and health databases offer new possibilities for evaluating chemical toxicity as an integrated system and overcome the limited predictivity of current fragmented efforts; yet, few studies have combined the new data streams. This dissertation tested the hypothesis that integrative computational toxicology approaches drawing upon diverse data sources would improve the prediction and interpretation of chemically induced diseases. First, chemical structures and toxicogenomics data were used to predict hepatotoxicity. Compared with conventional cheminformatics or toxicogenomics models, interpretation was enriched by the chemical and biological insights even though prediction accuracy did not improve. This motivated the second project that developed a novel integrative method, chemical-biological read-across (CBRA), that led to predictive and interpretable models amenable to visualization. CBRA was consistently among the most accurate models on four chemical-biological data sets. It highlighted chemical and biological features for interpretation and the visualizations aided transparency. Third, we developed an integrative workflow that interfaced cheminformatics prediction with pharmacoepidemiology validation using a case study of Stevens Johnson Syndrome (SJS), an adverse drug reaction (ADR) of major public health concern. Cheminformatics models first predicted potential SJS inducers and non-inducers, prioritizing them for subsequent pharmacoepidemiology evaluation, which then confirmed that predicted non-inducers were statistically associated with fewer SJS occurrences. By combining cheminformatics' ability to predict SJS as soon as drug structures are known, and pharmacoepidemiology's statistical rigor, we have provided a universal scheme for more effective study of SJS and other ADRs. Overall, this work demonstrated that integrative approaches could deliver more predictive and interpretable models. These models can then reliably prioritize high risk chemicals for further testing, allowing optimization of testing resources. A broader implication of this research is the growing role we envision for integrative methods that will take advantage of the various emerging data sources.Doctor of Philosoph

Integrative Systems Approaches Towards Brain Pharmacology and Polypharmacology

Polypharmacology is considered as the future of drug discovery and emerges as the next paradigm of drug discovery. The traditional drug design is primarily based on a “one target-one drug” paradigm. In polypharmacology, drug molecules always interact with multiple targets, and therefore it imposes new challenges in developing and designing new and effective drugs that are less toxic by eliminating the unexpected drug-target interactions. Although still in its infancy, the use of polypharmacology ideas appears to already have a remarkable impact on modern drug development. The current thesis is a detailed study on various pharmacology approaches at systems level to understand polypharmacology in complex brain and neurodegnerative disorders. The research work in this thesis focuses on the design and construction of a dedicated knowledge base for human brain pharmacology. This pharmacology knowledge base, referred to as the Human Brain Pharmacome (HBP) is a unique and comprehensive resource that aggregates data and knowledge around current drug treatments that are available for major brain and neurodegenerative disorders. The HBP knowledge base provides data at a single place for building models and supporting hypotheses. The HBP also incorporates new data obtained from similarity computations over drugs and proteins structures, which was analyzed from various aspects including network pharmacology and application of in-silico computational methods for the discovery of novel multi-target drug candidates. Computational tools and machine learning models were developed to characterize protein targets for their polypharmacological profiles and to distinguish indications specific or target specific drugs from other drugs. Systems pharmacology approaches towards drug property predictions provided a highly enriched compound library that was virtually screened against an array of network pharmacology based derived protein targets by combined docking and molecular dynamics simulation workflows. The developed approaches in this work resulted in the identification of novel multi-target drug candidates that are backed up by existing experimental knowledge, and propose repositioning of existing drugs, that are undergoing further experimental validations

Continuing professional development - challenge for professional organization

Professions, as one of key sectors of social systems, bear a leading role in the existing social work organization. Free professions take up a special place and significance, all the way from Roman artes liberales to our times. Pharmaceutical profession, as one of the oldest, led by ethical principles, is regulated by postulates accepted by the profession members, and in modern times established through legislations. Typical determinants of the regulated professions, which also refer to pharmacists, as chamber members, are as follows: following ethical principles, specific skills and knowledge, professional development, autonomy at work, continuing improvement, competencies development, professional associations, licensing