Evans C. Coutinho

Anaplastic lymphoma kinase (ALK) is a tyrosine kinase receptor implicated in cancers like breast cancer, non-small cell lung carcinoma, and neuroblastoma. Crizotinib, an ALK inhibitor, initially succeeded as a treatment for such cancers before mutations caused its failure. We investigated the mechanism of resistance due to mutations like C1156Y, L1196M, G1269A, L1198F, and C1156Y/L1198F through validated computational methods and published experimental data. Our analysis reveals that these mutations do not reduce the binding affinity or alter the binding conformation of crizotinib, consistent with published X-ray crystallographic and biochemical data. This study is the first to show the conformational change in ALK's activation loop (\textbf{A-loop}) due to phosphorylation, as there is no experimental structure for phosphorylated ALK. Principal component analysis confirms that A-loop dynamics is primarily responsible for ALK activation, explaining the changes in catalytic efficiency due to phosphorylation or mutants like C1156Y and L1198F. Our results conclusively demonstrate that mutations like C1156Y influence A-loop dynamics, leading to an ATP-independent activation. Therefore, ATP competitive inhibitors like crizotinib will inevitably lose their therapeutic effectiveness without losing their binding affinity to ALK.

Cancer, the second leading cause of death worldwide, is a major health problem. Chemotherapy, radiation therapy and surgery are current treatments for cancer. Most anticancer drugs have severe toxic effects and are required to be administered in cycles to reduce toxicity and prevent resistance. Plant-based drugs have shown a potential for treatment of cancer, and various plant secondary metabolites have shown promising antitumor activity against several cancer cell lines, such as leukemia, colon cancer, prostate cancer, breast cancer and lung cancer. Vincristine, etoposide, topotecan and paclitaxel, which are of natural origin, are successfully used in clinical practice, and this has generated interest in natural compounds as anticancer agents. Some phytoconstituents like curcumin, piperine, allicin, quercetin and resveratrol have been extensively researched and reviewed. In the current study, we have reviewed several plants like Athyrium hohenackerianum, Aristolochia baetica, Boswellia serrata, Panax ginseng, Berberis vulgaris, Tanacetum parthenium, Glycine max, Combretum fragrans, Persea americana, Raphanus sativus, Camellia sinensis, and Nigella sativa for their source, key phytoconstituents, and anticancer activity along with their toxicity profile. Few phytoconstituents like boswellic acid, sulforaphane and ginsenoside showed excellent anticancer activity compared to standard drugs and are potential clinical candidates.

Withdrawal Statement: The authors have withdrawn this manuscript because the reviewers have suggested significant revision of the manuscript. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

Optimizing the pharmacokinetics (PK) of a drug candidate to support oral dosing is a key challenge in drug development. PK parameters are usually estimated from the concentration–time profile following intravenous administration; however, traditional methods are time-consuming and expensive. In recent years, quantitative structure–pharmacokinetic relationship (QSPKR), an in silico tool that aims to develop a mathematical relationship between the structure of a molecule and its PK properties, has emerged as a useful alternative to experimental testing. Due to the complex nature of the various processes involved in dictating the fate of a drug, the development of adequate QSPKR models that can be used in real-world pre-screening situations has proved challenging. Given the crucial role played by a molecule’s ionization state in determining its PK properties, this work aims to build predictive QSPKR models for PK parameters in humans using an ionization state-based strategy. We divide a high-quality dataset into clusters based on ionization state at physiological pH and build global and ion subset-based ‘local’ models for three major PK parameters: plasma clearance (CL), steady-state volume of distribution (VD_ss), and half-life (t_1/2). We use a robust methodology developed in our lab entitled ‘EigenValue ANalySis’ that accounts for the stereospecificity in drug disposition and use the support vector machine algorithm for model building. Our findings suggest that categorizing compounds in accordance with ionization state does not result in improved QSPKR models. The narrow ranges in the endpoints along with redundancies in the data adversely affect the ion subset-based QSPKR models. We suggest alternative approaches such as elimination route-based models that account for drug–transporter interactions for CL and chemotype-specific QSPKR for VD_ss. Graphical abstract

It has always been a challenge to develop interventional therapies for Mycobacterium tuberculosis. Over the years, several attempts at developing such therapies have hit a dead-end owing to rapid mutation rates of the tubercular bacilli and their ability to lay dormant for years. Recently, cytochrome bcc complex (QcrB) has shown some promise as a novel target against the tubercular bacilli, with Q203 being the first molecule acting on this target. In this paper, we report the deployment of several ML-based approaches to design molecules against QcrB. Machine learning (ML) models were developed based on a data set of 350 molecules using three different sets of molecular features, i.e., MACCS keys, ECFP6 fingerprints, and Mordred descriptors. Each feature set was trained on eight ML classifier algorithms and optimized to classify molecules accurately. The support vector machine-based classifier using the ECFP6 feature set was found to be the best classifier in this study. Further, screening of the known imidazopyridine amide inhibitors demonstrated that the model correctly classified the most potent molecules as actives, hence validating the model for future applications.

Microbial resistance to conventional antibiotics has led to a surge in antimicrobial peptide (AMP) rational design initiatives that rely heavily on algorithms with good prediction accuracy and sensitivity. We present a quantitative structure‐activity relationship (QSAR) approach for predicting activity of cathelicidins, an AMP family with broad‐spectrum activity. The best multiple linear regression model built against Escherichia coli ATCC 25922 could accurately predict activity of three rationally designed peptides CP, DP, and Mapcon, having high sequence similarity. On further experimental validation of the rationally designed peptides, CP was found to exhibit high antimicrobial activity with negligible hemolysis. Here, we provide CP, an AMP with potential therapeutic applications and a family‐based QSAR model for AMP prediction.