Computational Pharmacology Modelling: The RAFAGene Tuberculosis Pharmacokinetic as a case study Jumoke Soyemi, Ezekiel Adebiyi, and Olanrewaju Oyelade jumokesoyemi@yahoo.com ezekiel.adebiyi@covenantuniversity.edu.ng ola.oyelade@covenantuniversity.edu.ng Department of Computer and Information Science Covenant University, Nigeria   Drug data will be extracted from KEGG, FDA Adverse Event Reporting System (FEARS) and SIDER databases. Medical conditions and patient profile data will be obtained from the phases I and II of the RAFAgene trials on Tuberculosis. The intrinsic features of drugs will be based on chemical structures and the ATC taxonomy of drugs and the intrinsic features of ADRs will be based on the MedDRA taxonomy of ADRs. The chemical similarities between drugs will be computed using SIMCOMP, and the ATC taxonomy similarities between drugs and the MedDRA taxonomy similarities between ADRs will both be computed using the semantic similarity algorithm. semi-supervised Link Prediction Classifier (SLP) which is a semi-supervised learning algorithm will be employed. Background of the Study Pathogenesis of Tuberculosis Interaction Map Research Methodology Computational pharmacology applies both bioinformatics and computational biology to pharmacology to understand drug action on the body, its adverse effect, drug targets as well as drug design. [1]. The outcome of medicine on various systems of the body may result to what is known as Adverse Drug Reaction. Adverse drug reaction is an appreciably harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product, which predicts hazard from future administration and warrants prevention or specific treatment, or alteration of the dosage regimen, or withdrawal of the product. Most serious ADR can be classified as either; type A, where the underlying mechanism is dose dependent, or type B or idiosyncratic, where the event is not predictable from the normal pharmacology of the drug and is generally independent of dose [2][3]. ADR has been reported in [4] to cause 10-30% hospital admissions, 30-150 billion dollars annual costs, 180,000 life threatening or fatal ADRs annually, 50% of these could have been prevented. Also clinical trials of drugs use 1000s of patients. It is therefore critically important that prompt and exact identification of ADRs be done for the safety and development of public health. The conventional way of detecting ADRs in the pre and post market stages. In this research, our disease of interest is the tuberculosis which is a highly persistent pathogen that affects the world especially Africa. A key aspect of Tuberculosis (TB) is its ability to dramatically change its metabolism in different states. After infecting the alveolar macrophage, it shifts into an infectious state. It is not only difficult to work with TB because of its slow growth rate, but most in vitro models are inaccurate simulations of in vivo conditions. Its clinical presentation can be extremely varying and it can infect every organ system. Hence this research interest in RAFAgene trials, one of the NIH RAFAGene study targeted to uncover pharmacogenetics employing technologies such as genome-wide and targeted SNPs screening with in-vitro confirmation of the biological plausibility of the association between pharmacokinetic and genetic characterization. The overall goal of the NIH “RAFAgene” is to explain the complex relationship between TB pathogen, host and drug exposure in the pathogenesis of TB. Therefore, using the pharmacological network (updated with genetic analysis results), an important output is the computational elucidation of this complex relationship. Our work is expected to produce result that will save enormous time in the future development of new drugs against the TB pathogen in the human host. Interaction map showing n number of drugs, k number of proteins, m number of pathways, and h number of adverse drug reactions(ADRs). Source: [6] Jane and Darrell, 2013 Aim and Objective The aim of this research work is to employ computational technique(s) using pharmacological network to clarify/predict the complex relationship between TB pathogen, host and drug exposure in the pathogenesis of TB. Therefore, this aim will be realized through the following objectives: 1. To build a pharmacology network from OFLOTUB and RAFAGene phase I and phase II preclinical data respectively. 2. We then employ our computational technique(s) to objective 1 and compare the results with their existing biological results 3. The technique(s) developed is also used to predict the results of the clinical phase III of OFLOTUB and RAFAGene study. 4. Lastly, the biological validation of our result in objective (3) will be investigated. Contribution to Knowledge The contribution to knowledge in this research is a model that can be used to make informed decisions so as to reduce the rate of attrition for drugs in development and increase the number of drugs with an acceptable benefit/risk ratio with TB as case study. Also the result will save enormous time in the future development of new drugs against the TB pathogen in the human host. Source: [5] Claudio et al, 2014 ADME Bibliography [ [2] M. Pirmohamed, A. M. Breckenridge, N. R. Kitteringham , B. K. Park , “Adverse drug reactions”. Br.Med. J. 316:1295–98, 1998. [3] J. K. Aronson, R. E. Ferner, Clarification of terminology in drug safety. Drug Saf. 28:851–70, 2005. [4] Sriram Natarajan et al, Identifying Adverse Drug Events using Relational learning. Indian University, 2012 [7] Fan Yang, Xiaohui Yu and George Karypis, “ Signaling Adverse Drug Reactions with Novel Feature-based Similarity model Research Methodology A bipartite network of drug-ADRs of this research will be constructed using both the intrinsic and topological features. The intrinsic features composing of patient profile (age, gender, genetic variability and indications), medical condition and drugs will be obtained. The intrinsic features will be obtained from chemical structures or biological functions of drugs or ADRs. Acknowledgement AUC shows the amount of drug absorbed Our special thanks to H3Abionet for sponsoring this work. Presented at ISCB Africa ASBCB Conference on Bioinformatics. Dar es Salaam, Tanzania. March 9-11, 2015