While drug discovery scientists take heed of various guidelines concerning drug-like character, the influence of acid/base properties often remains under-scrutinised. Ionisation constants (pKa values) are fundamental to the variability of the biopharmaceutical characteristics of drugs and to underlying parameters such as logD and solubility. pKa values affect physicochemical properties such as aqueous solubility, which in turn influences drug formulation approaches. More importantly, absorption, distribution, metabolism, excretion and toxicity (ADMET) are profoundly affected by the charge state of compounds under varying pH conditions. Consideration of pKa values in conjunction with other molecular properties is of great significance and has the potential to be used to further improve the efficiency of drug discovery. Given the recent low annual output of new drugs from pharmaceutical companies, this review will provide a timely reminder of an important molecular property that influences clinical success.

Underpinning all drug discovery projects is the interaction between a drug and its target, usually a protein. Thus, improved methods for predicting the magnitude of protein-ligand interactions have the potential to improve the efficiency of drug development. In this review, we describe the principles of free energy methods used for the calculation of protein-ligand binding free energies, the challenges associated with these methods, and recent advances developed to address these difficulties. We then present case studies from 2005 to 2017, each demonstrating that alchemical free energy methods can assist rational drug design projects. We conclude that alchemical methods are becoming a feasible reality in medicinal chemistry research due to improved computational resources and algorithms and that alchemical free energy predictions methods are close to becoming a mainstream tool for medicinal chemists.

No abstract

A structure–activity relationship (SAR) guided design of novel tubulin polymerization inhibitors has resulted in a series of benzo[b]furans with exceptional potency toward cancer cells and activated endothelial cells. The potency of early lead compounds has been substantially improved through the synergistic effect of introducing a conformational bias and additional hydrogen bond donor to the pharmacophore. Screening of a focused library of potent tubulin polymerization inhibitors for selectivity against cancer cells and activated endothelial cells over quiescent endothelial cells has afforded 7-hydroxy-6-methoxy-2-methyl-3-(3,4,5-trimethoxybenzoyl)benzo-[b]furan (BNC105, 8) as a potent and selective antiproliferative. Because of poor solubility, 8 is administered as its disodium phosphate ester prodrug 9 (BNC105P), which is rapidly cleaved in vivo to return the active 8. 9 exhibits both superior vascular disrupting and tumor growth inhibitory properties compared with the benchmark agent combretastatin A-4 disodium phosphate 5 (CA4P).

We report the development of homology models of dopamine (D(2), D(3), and D(4)), serotonin (5-HT(1B), 5-HT(2A), 5-HT(2B), and 5-HT(2C)), histamine (H(1)), and muscarinic (M(1)) receptors, based on the high-resolution structure of the beta(2)-adrenergic receptor. The homology models were built and refined using Prime. We have addressed the required modeling of extracellular loop 2, which is often implicated in ligand binding. The orthosteric sites of the models were optimized using induced fit docking, to allow for side-chain flexibility, and the resulting receptor models have been evaluated using protein validation tools. Of the nine homology models developed, six models showed moderate to good enrichment in virtual screening experiments (5-HT(2A), 5-HT(1B), D(2), 5-HT(2C), D(3), and M(1)). The 5-HT(2A) receptor displayed the highest enrichment in virtual screening experiments with enrichment factors of 6.1, 6.9, and 5.9 at 2, 5, and 10%, respectively, of the screened database. However, three of the models require further refinement (5-HT(2B), D(4), and H(1)), due to difficulties in modeling some of the binding site residues as well as the extracellular loop 2. Our effort also aims to supplement the limited number of tested G protein-coupled receptor homology models based on the beta(2) crystal structure that are freely available to the research community.