Inherent efficacies of pyrazole-based derivatives for cancer therapy: the interface between experiment and in silico.

There has been an increasing trend in the design of novel pyrazole derivatives for desired biological applications. For a cost-effective strategy, scientists have implemented various computational drug design tools to go hand in hand with experiments for the design and discovery of potentially effective pyrazole-based therapeutics. This review highlights the milestones of pyrazole-containing inhibitors and the use of molecular modeling techniques in conjunction with experimental studies to provide a view of the binding mechanism of these compounds. The review focuses on the established targets that play a key role in cancer therapy, including proteins involved in tubulin polymerization, carbonic anhydrase and tyrosine kinase. Overall, using both experimental and computational methods in drug design represents a promising approach to cancer therapy.

Exploring the effect of ritonavir and TMC-310911 on SARS-CoV-2 and SARS-CoV main proteases: potential from a molecular perspective.

AIM: As coronavirus (CoV) disease 2019-associated pneumonia spreads globally, there has been an urgent need to combat the spread and develop vaccines. MATERIALS & METHODS: We used an integrated computational algorithm to explore the binding mechanism of TMC-310911/ritonavir (RVT) with SARS-CoV-2 and SARS-CoV main proteases. RESULTS: RVT and TMC-310911 had favorable interactions with the proteases, and these high interactions are facilitated by some significant residues such as Asn133, Gly195 and Gln192. Our study further implicated two important rings in the structure of RVT as a possible chemical culprit in its therapeutic activity. CONCLUSION: Although there are conflicting clinical results on the therapeutic potency of RVT in the treatment of coronavirus disease 2019, our findings provided molecular insight into the binding mechanism of TMC-310911 and RVT with SARS-CoV-2 and SARS-CoV main proteases.

'Piperazining' the catalytic gatekeepers: unraveling the pan-inhibition of SRC kinases; LYN, FYN and BLK by masitinib.

Aim: Blocking oncogenic signaling of B-cell receptor (BCR) has been explored as a viable strategy in the treatment of diffuse large B-cell lymphoma. Masitinib is shown to multitarget LYN, FYN and BLK kinases that propagate BCR signals to downstream effectors. However, the molecular mechanisms of its selectivity and pan-inhibition remain elusive. Materials & methods: This study therefore employed molecular dynamics simulations coupled with advanced post-molecular dynamics simulation techniques to unravel the structural mechanisms that inform the reported multitargeting ability of masitinib. Results: Molecular dynamics simulations revealed initial selective targeting of catalytic residues (Asp334/Glu335 - LYN; Asp130/Asp148/Glu54 - FYN; Asp89 - BLK) by masitinib, with high-affinity interactions via its piperazine ring at the entrance of the ATP-binding pockets, before systematic access into the hydrophobic deep pocket grooves. Conclusion: Identification of these 'gatekeeper' residues could open up a novel paradigm of structure-based design of highly selective pan-inhibitors of BCR signaling in the treatment of diffuse large B-cell lymphoma.

Co-inhibition as a strategic therapeutic approach to overcome rifampin resistance in tuberculosis therapy: atomistic insights.

AIM: Amid the current global challenge of antimicrobial resistance, RNA polymerase remains a paramount therapeutic target for tuberculosis. Dual binding of rifampin (RIF) and a novel compound, DAAP1, demonstrated the suppression of RIF resistance. However, a paucity of data elucidating the structural mechanism of action of this synergistic interaction prevails. Methodology & results: Molecular dynamic simulations unraveled the synergistic inhibitory characteristics of DAAP1 and RIF. Co-binding induced a stable protein, increased the degree of compactness of binding site residues around RIF and subsequently an improved binding affinity toward RIF. CONCLUSION: Findings established the structural mechanism by which DAAP1 stabilizes Mycobacterium tuberculosis RNA polymerase, thus possibly suppressing RIF resistance. This study will assist toward the design of novel inhibitors combating drug resistance in tuberculosis.

Reversible versus irreversible inhibition modes of ERK2: a comparative analysis for ERK2 protein kinase in cancer therapy.

AIM: Irreversible covalent drug inhibition is an emerging paradigm; however, critical gaps in unraveling the efficacy of molecular determinants still persist. METHODOLOGY: We compare two ERK2 inhibitors with different binding modes. A 5-7-Oxozeaenol is selective inhibitor which irreversibly binds ERK2 by the formation of covalent bond with Cys166 while 5-iodotubercidin binds noncovalently. Result & discussion: Covalent inhibition showed greater protein stability, favorable binding energetics (irreversible inhibition binding free energy [ΔGbind] = -40.4354 kcal/mol and reversible inhibition ΔGbind = -26.2515 kcal/mol); higher correlation in residual movement and multiple van der Waals interactions as evident from residue interaction analysis. CONCLUSION: This investigation of the different inhibition modes of ERK2 would assist toward the design of more potent and highly site-specific covalent inhibitors in cancer therapy.

Hybrid 2D/3D-quantitative structure-activity relationship and modeling studies perspectives of pepstatin A analogs as cathepsin D inhibitors.

AIM: Cathepsin D, one of the attractive targets in the treatment of breast cancer, has been implicated in HIV neuropathogenesis with potential proteolytic effects on chemokines. Methodology/result: Diverse modeling tools were used to reveal the key structural features affecting the inhibitory activities of 78 pepstatin A analogs. Analyses were performed to investigate the stability, rationality and fluctuation of the analogs. Results showed a clear correlation between the experimental and predicted activities of the analogs as well as the variation in their activities relative to structural modifications. CONCLUSION: The insight gained from this study offers theoretical references for understanding the mechanism of action of cathepsin D and will aid in the design of more potent and clinically-relevant drugs. Graphical abstract [Formula: see text].

Novel quinazolinone-based 2,4-thiazolidinedione-3-acetic acid derivatives as potent aldose reductase inhibitors.

AIM: Targeting aldose reductase enzyme with 2,4-thiazolidinedione-3-acetic acid derivatives having a bulky hydrophobic 3-arylquinazolinone residue. MATERIALS & METHODS: All the target compounds were structurally characterized by different spectroscopic methods and microanalysis, their aldose reductase inhibitory activities were evaluated, and binding modes were studied by molecular modeling. RESULTS: All the synthesized compounds proved to inhibit the target enzyme potently, exhibiting IC50 values in the nanomolar/low nanomolar range. Compound 5i (IC50 = 2.56 nM), the most active of the whole series, turned out to be almost 70-fold more active than the only marketed aldose reductase inhibitor epalrestat. CONCLUSION: This work represents a promising matrix for developing new potential therapeutic candidates for prevention of diabetic complications through targeting aldose reductase enzyme. [Formula: see text].

Quantum mechanics implementation in drug-design workflows: does it really help?

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

Tailored-pharmacophore model to enhance virtual screening and drug discovery: a case study on the identification of potential inhibitors against drug-resistant Mycobacterium tuberculosis (3R)-hydroxyacyl-ACP dehydratases.

AIM: Virtual screening (VS) is powerful tool in discovering molecular inhibitors that are most likely to bind to drug targets of interest. Herein, we introduce a novel VS approach, so-called 'tailored-pharmacophore', in order to explore inhibitors that overcome drug resistance. Methodology & results: The emergence and spread of drug resistance strains of tuberculosis is one of the most critical issues in healthcare. A tailored-pharmacophore approach was found promising to identify in silico predicted hit with better binding affinities in case of the resistance mutations in MtbHadAB as compared with thiacetazone, a prodrug used in the clinical treatment of tuberculosis. CONCLUSION: This approach can potentially be enforced for the discovery and design of drugs against a wide range of resistance targets.

Per-residue energy decomposition pharmacophore model to enhance virtual screening in drug discovery: a study for identification of reverse transcriptase inhibitors as potential anti-HIV agents.

A novel virtual screening approach is implemented herein, which is a further improvement of our previously published "target-bound pharmacophore modeling approach". The generated pharmacophore library is based only on highly contributing amino acid residues, instead of arbitrary pharmacophores, which are most commonly used in the conventional approaches in literature. Highly contributing amino acid residues were distinguished based on free binding energy contributions obtained from calculation from molecular dynamic (MD) simulations. To the best of our knowledge; this is the first attempt in the literature using such an approach; previous approaches have relied on the docking score to generate energy-based pharmacophore models. However, docking scores are reportedly unreliable. Thus, we present a model for a per-residue energy decomposition, constructed from MD simulation ensembles generating a more trustworthy pharmacophore model, which can be applied in drug discovery workflow. This work is aimed at introducing a more rational approach to the field of drug design, rather than comparing the validity of this approach against those previously reported. We recommend additional computational and experimental work to further validate this approach. This approach was used to screen for potential reverse transcriptase inhibitors using the pharmacophoric features of compound GSK952. The complex was subjected to docking, thereafter, MD simulation confirmed the stability of the system. Experimentally determined inhibitors with known HIV-reverse transcriptase inhibitory activity were used to validate the protocol. Two potential hits (ZINC46849657 and ZINC54359621) showed a significant potential with regard to free binding energy. Reported results obtained from this work confirm that this new approach is favorable in the future of the drug design industry.