The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein 'parallel' information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the 'parallel progression approach'. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log Vd) and clearance (log Cl), which together influence the half-life (t1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pKi have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pKi values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the 'parallel progression approach' helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.

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We present EigenValue ANalySis (EVANS), a QSPR methodology that considers 3D molecular information of enantiomeric ensembles of chiral molecules without the need to perform an alignment step. EVANS follows an intricate molecular modelling protocol that generates orthogonal eigenvalues from hybrid matrices of physicochemical properties and 3D structure; these eigenvalues are used as independent variables in QSPR analyses. The EVANS formalism has been presented and deployed to build quantitative structure pharmacokinetic relationship (QSPKR) models on a benchmark dataset for three critical PK parameters: steady-state volume of distribution (VDss), clearance (CL), and half-life (t1/2). Predictive QSPKR models were built by using the eigenvalues generated via the EVANS methodology in conjunction with multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms, and it was observed that the EVANS QSPKR models sync with published work in the literature. Thus, we present the EVANS methodology as a first-line prediction tool to prioritise compounds in drug discovery and development.

We present EigenValue ANalySis (EVANS), a QSPR methodology that considers 3D molecular information of enantiomeric ensembles of chiral molecules without the need to perform an alignment step. EVANS follows an intricate molecular modelling protocol that generates orthogonal eigenvalues from hybrid matrices of physicochemical properties and 3D structure; these eigenvalues are used as independent variables in QSPR analyses. The EVANS formalism has been presented and deployed to build quantitative structure pharmacokinetic relationship (QSPKR) models on a benchmark dataset for three critical PK parameters: steady-state volume of distribution (VDss), clearance (CL), and half-life (t1/2). Predictive QSPKR models were built by using the eigenvalues generated via the EVANS methodology in conjunction with multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms, and it was observed that the EVANS QSPKR models sync with published work in the literature. Thus, we present the EVANS methodology as a first-line prediction tool to prioritise compounds in drug discovery and development.