Predictive hybrid paradigm for cytotoxic activity of 1,3,4-thiadiazole derivatives as CDK6 inhibitors against human (MCF-7) breast cancer cell line and its structural modifications: rational for novel cancer therapeutics.

The dysregulation of cyclin-CDK6 interactions has been implicated in human breast cancer, providing a rationale for more therapeutic options. Recently, ATP-competitive inhibitors have been employed for managing breast cancer. These molecules, like most natural CDKs inhibitors, potently bind in the ATP-binding site of CDK6 to regulate trans-activation. Nonetheless, only a few numbers of these molecules are approved to mitigate breast cancer, thus, ensuring that the search for more selective inhibitors continues. In this study, we attempted to establish the selective predictive models for identifying potent CDK6 inhibitors against a human breast cancer cell-line using a dataset of fifty-two 1,3,4-thiadiazole derivatives. The significant eight descriptor hybrid QSAR models generated exhibited encouraging statistical attributes including R2> 0.70, Q2LOO > 0.70, Q2LMO > 0.60, Qfn2 > 0.6. Furthermore, the study designed new compounds based on the activity and structural basis for selectivity of compounds for CDK6. While demonstrating good potency and modest selectivity, the compound C16, which showed significantly high activity of 5.5607 µM and binding energy value of -9.0 Kcal/mol, was used as template for compounds design to generate 10 novel series of 1,3,4-thiadiazole analogues containing benzisoselenazolone scaffolds, with significant pharmacological activity and better selectivity for CDK6. By our rationale, four of the designed compounds (C16b, C16h, C16i, and C16j) with activity values of 6.2584 µM, 6.7812 µM, 6.4717 µM, and 6.2666 µM respectively, and binding affinities of -10.0 kcal/mol, -9.9 kcal/mol, -9.9 kcal/mol, and -9.9 kcal/mol respectively, may emerge as therapeutic options for breast cancer treatment after extensive in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

Targeting p53-MDM2 interactions to identify small molecule inhibitors for cancer therapy: beyond "Failure to rescue".

At present, disrupting p53-MDM2 interactions through small molecule ligands is a promising approach to safe treatment and management of human cancer. Tumor cells unlike the normal cells, are rapidly evolving affecting the efficacy of many approved anti-cancer agents due to drug resistance. Therefore, identifying a potential anticancer compound is crucial. Pharmacophore based virtual screening, followed by molecular docking, ADMET evaluation, and molecular dynamics studies against MDM2 protein was investigated to identify potential ligands that may act as inhibitors. The model (AHRR_1) with survival score (4.176) was selected among the top ranked generated Pharmacophore hypothesis. Validation of the model hypothesis by an external dataset of actives and inactive compounds produced significant validation attributes including; AUC = 0.85, BEDROC = 0.56 at α = 20.0, RIE = 8.18, AUAC = 0.88, and EF of 6.2 at the top 2% of the dataset. The model was use for screening the ZINC database, and the top 1375 hits satisfying the model hypothesis were subjected to molecular docking studies to understand the molecular and structural basis of selectivity of compounds for MDM2 protein. A sub-set of 25 compounds with binding energy lower than the reference inhibitors were evaluated for pharmacokinetic properties. Four compounds (ZINC02639178, ZINC06752762, ZINC38933175, and ZINC77969611) showed the most desired pharmacokinetic profile. Lastly, investigation of the dynamic behaviour of leads-protein complexes through MD simulation showed similar RMSD, RMSF, and H-bond occupancy profile compared to a reference inhibitor, suggesting stability throughout the simulation time. However, ZINC02639178 was found to satisfy the molecular enumeration the most compared to the other three leads. It may emerge as potential treatment option after extensive experimental studies. Communicated by Ramaswamy H. Sarma.

In silico design and validation of a highly degenerate primer pair: a systematic approach.

BACKGROUND: The techniques of amplifying genetic materials have enabled the extensive study of several biological activities outside the biological milieu of living systems. More recently, this approach has been extended to amplify population of genes, from evolutionarily related gene family for detection and evaluation of microbial consortial with several unique potentialities (e.g., enzymatic degradability). Conceivably, primer mixtures containing substitutions of different bases at specific sites (degenerate primers) have enabled the amplification of these genes in PCR reaction. However, the degenerate primer design problem (DPD) is a constraint to designing this kind of primer. To date, different algorithms now exist to solve various versions of DPD problem, many of which, only few addresses and satisfy the criteria to design primers that can extensively cover high through-put sequences while striking the balance between specificity and efficiency. The highly degenerate primer (HYDEN) design software program primarily addresses this variant of DPD problem termed "maximum coverage-degenerate primer design (MC-DPD)" and its heuristics have been substantiated for optimal efficiency from significant successes in PCR. In spite of the premium presented for designing degenerate primers, literature search has indicated relatively little use of its heuristics. This has been thought to result from the complexity of the program since it is run only by command-line, hence limiting its accessibility. To solve this problem, researchers have optionally considered the manual design of degenerate primers or design through software programs that provides accessibility through a graphical user interface (GUI). Realizing this, we have attempted in this study to provide a user-friendly approach for researchers with little or no background in bioinformatics to design degenerate primers using HYDEN RESULTS: Virtual Tests of our designed degenerate primer pair through in silico PCR substantiated the correspondence between efficiency and coverage with the target sequences as pre-defined by the initial HYDEN output, thereby validating the potentials of HYDEN to effectively solve the MC-DPD problem. Additionally, the designed primer-pair mechanistically amplified all sequences used as a positive control with no amplification observed in the negative controls. CONCLUSION: In this study, we provided a turnkey protocol to simplify the design of degenerate primers using the heuristics of the HYDEN software program.