Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand
A crucial factor for the approval and success of any drug is how it behaves in the body. Many drugs, however, do not reach the market due to poor efficacy or unacceptable side effects. It is therefore important to take these into consideration early in the drug development process, both in the prioritization of potential hits, and optimization of lead compounds. In silico approaches offer a cost and time-effective approach to rapidly screen and optimize pharmacokinetic and toxicity properties. Here we demonstrate the use of the comprehensive analysis system pkCSM, to allow early identification of potential problems, prioritization of hits, and optimization of leads.
Key wordsADMET predictions Computational medicinal chemistry Drug development Hit prioritization Lead optimization Pharmacokinetics Toxicity
This work was funded by the Jack Brockhoff Foundation (JBF 4186, 2016) and a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MRC) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (MR/M026302/1). This research was supported by the Victorian Life Sciences Computation Initiative (VLSCI), an initiative of the Victorian Government, Australia, on its Facility hosted at the University of Melbourne (UOM0017). D.E.V.P. received support from the René Rachou Research Center (CPqRR/FIOCRUZ Minas), Brazil. LMK was supported by a RD Wright Biomedical Career Development Fellowship from the National Health and Medical Research Council of Australia (APP1105383). DBA is supported by a C. J. Martin Research Fellowship from the National Health and Medical Research Council of Australia (APP1072476), and the Department of Biochemistry, University of Melbourne.
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