Evolution and intelligent design in drug development

Receptor-based pharmacophore modeling, molecular docking, synthesis and biological evaluation of novel VEGFR-2, FGFR-1, and BRAF multi-kinase inhibitors

A receptor-based pharmacophore model describing the binding features required for the multi-kinase inhibition of the target kinases (VEGFR-2, FGFR-1, and BRAF) were constructed and validated. It showed a good overall quality in discriminating between the active and the inactive in a compiled test set compounds with F1 score of 0.502 and Mathew's correlation coefficient of 0.513. It described the ligand binding to the hinge region Cys or Ala, the glutamate residue of the Glu-Lys αC helix conserved pair, the DFG motif Asp at the activation loop, and the allosteric back pocket next to the ATP binding site. Moreover, excluded volumes were used to define the steric extent of the binding sites. The application of the developed pharmacophore model in virtual screening of an in-house scaffold dataset resulted in the identification of a benzimidazole-based scaffold as a promising hit within the dataset. Compounds 8a-u were designed through structural optimization of the hit benzimidazole-based scaffold through (un)substituted aryl substitution on 2 and 5 positions of the benzimidazole ring. Molecular docking simulations and ADME properties predictions confirmed the promising characteristics of the designed compounds in terms of binding affinity and pharmacokinetic properties, respectively. The designed compounds 8a-u were synthesized, and they demonstrated moderate to potent VEGFR-2 inhibitory activity at 10 µM. Compound 8u exhibited a potent inhibitory activity against the target kinases (VEGFR-2, FGFR-1, and BRAF) with IC50 values of 0.93, 3.74, 0.25 µM, respectively. The benzimidazole derivatives 8a-u were all selected by the NCI (USA) to conduct their anti-proliferation screening. Compounds 8a and 8d resulted in a potent mean growth inhibition % (GI%) of 97.73% and 92.51%, respectively. Whereas compounds 8h, 8j, 8k, 8o, 8q, 8r, and 8u showed a mean GI% > 100% (lethal effect). The most potent compounds on the NCI panel of 60 different cancer cell lines were progressed further to NCI five-dose testing. The benzimidazole derivatives 8a, 8d, 8h, 8j, 8k, 8o, 8q, 8r and 8u exhibited potent anticancer activity on the tested cell lines reaching sub-micromolar range. Moreover, 8u was found to induce cell cycle arrest of MCF-7 cell line at the G2/M phase and accumulating cells at the sub-G1 phase as a result of cell apoptosis.

Discovery of dual kinase inhibitors targeting VEGFR2 and FAK: structure-based pharmacophore modeling, virtual screening, and molecular docking studies

VEGFR2 and FAK signaling pathways are interconnected and have synergistic effects on tumor angiogenesis, growth, and metastasis. Thus, instead of the conventional targeting of each of these proteins individually with a specific inhibitor, the present work aimed to discover novel dual inhibitors targeting both VEGFR2 and FAK exploiting their association. To this end, receptor-based pharmacophore modeling technique was opted to generate 3D pharmacophore models for VEGFR2 and FAK type II kinase inhibitors. The generated pharmacophore models were validated by assessing their ability to discriminate between active and decoy compounds in a pre-compiled test set of VEGFR2 and FAK active compounds and decoys. ZINCPharmer web tool was then used to screen the ZINC database purchasable subset using the validated pharmacophore models retrieving 42,616 hits for VEGFR2 and 28,475 hits for FAK. Subsequently, they were filtered using various filters leaving 13,023 and 6,832 survived compounds for VEGFR2 and FAK, respectively, with 124 common compounds. Based on molecular docking simulations, thirteen compounds were found to satisfy all necessary interactions with VEGFR2 and FAK kinase domains. Thus, they are predicted to have a possible dual VEGFR2/FAK inhibitory activity. Finally, SwissADME web tool showed that compound ZINC09875266 is not only promising in terms of binding pattern to our target kinases, but also in terms of pharmacokinetic properties.

Options to Improve the Action of PROTACs in Cancer: Development of Controlled Delivery Nanoparticles

Classical targeting in cancer focuses on the development of chemical structures able to bind to protein pockets with enzymatic activity. Some of these molecules are designed to bind the ATP side of the kinase domain avoiding protein activation and the subsequent oncogenic activity. A further improvement of these agents relies on the generation of non-allosteric inhibitors that once bound are able to limit the kinase function by producing a conformational change at the protein and, therefore, augmenting the antitumoural potency. Unfortunately, not all oncogenic proteins have enzymatic activity and cannot be chemically targeted with these types of molecular entities. Very recently, exploiting the protein degradation pathway through the ubiquitination and subsequent proteasomal degradation of key target proteins has gained momentum. With this approach, non-enzymatic proteins such as Transcription Factors can be degraded. In this regard, we provide an overview of current applications of the PROteolysis TArgeting Chimeras (PROTACs) compounds for the treatment of solid tumours and ways to overcome their limitations for clinical development. Among the different constraints for their development, improvements in bioavailability and safety, due to an optimized delivery, seem to be relevant. In this context, it is anticipated that those targeting pan-essential genes will have a narrow therapeutic index. In this article, we review the advantages and disadvantages of the potential use of drug delivery systems to improve the activity and safety of PROTACs.

Enhanced Sampling Approach to the Induced-Fit Docking Problem in Protein-Ligand Binding: The Case of Mono-ADP-Ribosylation Hydrolase Inhibitors

Enhanced sampling methods can predict free-energy landscapes associated with protein/ligand binding, characterizing the involved intermolecular interactions in a precise way. However, these in silico approaches can be challenged by induced-fit effects. Here, we present a variant of volume-based metadynamics tailored to tackle this problem in a general and efficient way. The validity of the approach is established by applying it to substrate/enzyme complexes of pharmacological relevance: mono-ADP-ribose (ADPr) in complex with mono-ADP-ribosylation hydrolases (MacroD1 and MacroD2), where induced-fit phenomena are known to be significant. The calculated binding free energies are consistent with experiments, with an absolute error smaller than 0.5 kcal/mol. Our simulations reveal that in all circumstances, the active loops, delimiting the boundaries of the binding site, undergo significant conformation rearrangements upon ligand binding. The calculations further provide, for the first time, the molecular basis of ADPr specificity and the relative changes in its experimental binding affinity on passing from MacroD1 to MacroD2 and all its mutants. Our study paves the way to the quantitative description of induced-fit events in molecular recognition.

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies.

A superposition free method for protein conformational ensemble analyses and local clustering based on a differential geometry representation of backbone

Here a differential geometry (DG) representation of protein backbone is explored on the analyses of protein conformational ensembles. The protein backbone is described by curvature, κ, and torsion, τ, values per residue and we propose 1) a new dissimilarity and protein flexibility measurement and 2) a local conformational clustering method. The methods were applied to Ubiquitin and c-Myb-KIX protein conformational ensembles and results show that κ\τ metric space allows to properly judge protein flexibility by avoiding the superposition problem. The d_max measurement presents equally good or superior results when compared to RMSF, especially for the intrinsically unstructured protein. The clustering method is unique as it relates protein global to local dynamics by providing a global clustering solutions per residue. The methods proposed can be especially useful to the analyses of highly flexible proteins. The software written for the analyses presented here is available at https://github.com/AMarinhoSN/FleXgeo for academic usage only.

Combating mutations in genetic disease and drug resistance: understanding molecular mechanisms to guide drug design

INTRODUCTION

Mutations introduce diversity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.