Bioactivity predictions and virtual screening using machine learning predictive model.

Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatability in available models and chemical bias issues constrain drug discovery and development. A new prediction model for enzyme inhibitors has been developed, and the model efficacy was checked using Dipeptidyl peptidase 4 (DPP-4) inhibitors. A Python script was prepared and can be provided for personal use upon request. Among various machine learning algorithms, it was found that Random Forest offers the best accuracy. Two models were compared, one with diverse training and test data and the other with a random split. It was concluded that machine learning predictive models based on the Murcko scaffold can address chemical bias concerns. In-silico screening of the Drug Bank database identified two molecules against DPP-4, which are previously proven hit molecules. The approach was further validated through molecular docking studies and molecular dynamics simulations, demonstrating the credibility and relevance of the developed model for future investigations and potential translation into clinical applications.Communicated by Ramaswamy H. Sarma.

The first clinical validation of whole-genome screening on standard trophectoderm biopsies of preimplantation embryos.

Objective: To validate the performance of our laboratory-developed whole-genome screening assay within clinical preimplantation genetic testing environments. Design: Perform a laboratory-developed whole-genome assay on both cell lines and trophectoderm biopsies, subsequently employing the next-generation sequencing procedure to reach a sequencing depth of 30X. Adhere to the Genome Analysis Toolkit best practices for accuracy, sensitivity, specificity, and precision calculations by comparing samples with references. Our assay was then applied to cell lines and biopsies harboring known pathogenic variants, aiming to ascertain these changes solely from the next-generation sequencing data, independent of parental genome information. Settings: Clinical laboratory. Patients: Coriell cell lines and research embryos with known chromosomal or genetic variants. Research trophectoderm biopsies from a couple that are heterozygous carriers for distinct variants in the same autosomal recessive gene (HOGA1). Intervention: Not applicable. Main Outcome Measures: Accuracy, sensitivity, specificity, and precision were assessed by comparing the samples to their references. For samples with known variants, we calculated our sensitivity to detecting established variants. For the research embryos, noncarrier, carrier, and compound heterozygous states of inherited HOGA1 variants were distinguished independently of parental samples. Results: Amplification of DNA from cell lines and embryos yielded success rates exceeding 99.9% and 98.2%, respectively, although maintaining an accuracy of >99.9% for aneuploidy assessment. The accuracy (99.99%), specificity (99.99%), sensitivity (98.0%), and precision (98.1%) of amplified genome in the bottle (reference NA12878) and embryo biopsies were comparable to results on genomic DNA, including mitochondrial heteroplasmy. Using our assay, we achieved >99.99% sensitivity when examining samples with known chromosomal and genetic variants. This encompassed pathogenic CFTR, BRCA1, and other variants, along with uniparental isodisomies and microdeletions such as DiGeorge syndrome. Our research study identified noncarrier, carrier, and compound heterozygous states within trophectoderm biopsies while simultaneously screening for 1,300 other severe monogenic diseases. Conclusion: To our knowledge, this is the first clinical validation of whole-genome embryo screening. In this study, we demonstrated high accuracy for aneuploidy calls (>99.9%) and genetic variants (99.99%), even in the absence of parental genomes. This assay demonstrates advancements in genomic screening and an extended scope for testing capabilities in the realm of preimplantation genetic testing.

FDA approved fused-pyrimidines as potential PI3K inhibitors: a computational repurposing approach.

Fused pyrimidine scaffold is present in several US FDA-approved drugs for various therapeutic indications. Drug repurposing (or drug repositioning) involves the analysis of existing clinically approved drugs for new therapeutic indications. Phosphoinositide-3-kinase (PI3K), via the regulatory PI3K pathway, is involved in cell growth, proliferation, differentiation, survival, and angiogenesis. It is also considered a target in anticancer drug development as it promotes the growth of cancerous cells and increases resistance to anticancer therapy. The present work employed computational techniques like molecular docking, MMGBSA analysis, and molecular dynamics simulations to explore the PI3K inhibition by FDA-approved drugs with fused pyrimidine scaffold. The work identifies Lapatinib as a pan-class I PI3K inhibitor and Dipyridamole as an γ isoform-specific PI3K inhibitor and is reported here.Communicated by Ramaswamy H. Sarma.