Estimation of Maximum Recommended Therapeutic Dose Using Predicted Promiscuity and Potency

Synthesis, anticancer evaluation of novel hybrid pyrazole-based chalcones, molecular docking, DNA fragmentation, and gene expression: in vitro studies

A unique series of pyrazolyl-chalcone derivatives was synthesized via the method of Claisen-Schmidt condensation. The desired chalcone derivatives 7a-d and 9a-f were obtained in good yields by reacting the 4-acetyl-5-thiophene-pyrazole with the appropriate heteroaryl aldehyde derivatives. The novel chalcones have undergone complete elemental analysis, 1H-NMR, 13C-NMR, mass spectrometry, and IR characterization. The three human cancer cell lines MCF7 (human Caucasian breast adenocarcinoma), PC3 (prostatic cancer) and PACA2 (pancreatic carcinoma) as well as the normal cell line BJ1 (normal skin fibroblasts) were tested in vitro for the anti-cancer properties of the newly synthesized chalcone derivatives. When compared to the reference medicine doxorubicin (IC50 = 52.1 μM), compound 9e showed the most promise derivative (IC50 = 27.6 μM) against PACA2 cells, while compound 7d demonstrated anticancer efficacy (IC50 = 42.6 μM against MCF7 cells compared to the reference drug doxorubicin (IC50 = 48 μM). Using breast and pancreatic cell lines, the gene expression, DNA damage, and DNA fragmentation percentages for compounds 7d and 9e were evaluated. Moreover, the molecular docking study of compounds 7d and 9e was assessed. The binding affinities of compound 9e toward P53 mutant Y220C was -22 kcal per mole, while those of compound 7d towards Bcl2 and CDK4 were -27.81 and -26.9 kcal per mole, respectively, compared to the standard values (-15.82, -33.96 and -29.9 kcal per mole).

Homology modeling of TMPRSS2 yields candidate drugs that may inhibit entry of SARS-CoV-2 into human cells

The most rapid path to discovering treatment options for the novel coronavirus SARS-CoV-2 is to find existing medications that are active against the virus. We have focused on identifying repurposing candidates for the transmembrane serine protease family member II (TMPRSS2), which is critical for entry of coronaviruses into cells. Using known 3D structures of close homologs, we created seven homology models. We also identified a set of serine protease inhibitor drugs, generated several conformations of each, and docked them into our models. We used three known chemical (non-drug) inhibitors and one validated inhibitor of TMPRSS2 in MERS as benchmark compounds and found six compounds with predicted high binding affinity in the range of the known inhibitors. We also showed that a previously published weak inhibitor, Camostat, had a significantly lower binding score than our six compounds. All six compounds are anticoagulants with significant and potentially dangerous clinical effects and side effects. Nonetheless, if these compounds significantly inhibit SARS-CoV-2 infection, they could represent a potentially useful clinical tool.

PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

Impact of Physicochemical Properties on Dose and Hepatotoxicity of Oral Drugs

A database containing maximum daily doses of 1841 marketed oral drugs was used to examine the influence of physicochemical properties on dose and hepatotoxicity (drug induced liver injury, DILI). Drugs in the highest ∼20% dose range had significantly reduced mean lipophilicity and molecular weight, increased fractional surface area, increased % of acids, and decreased % of bases versus drugs in the lower ∼60% dose range. Drugs in the ∼20-40% dose range had intermediate mean properties, similar to the mean values for the full drug set. Drugs that are both large and highly lipophilic almost invariably do not have doses in the upper ∼20% range. The results show that oral druglike physicochemical properties are different according to these dose ranges, and this is consistent with maintenance of acceptable safety profiles as efficacious exposure increases. Verified DILI annotations from a compilation of >1000 approved drugs (Chen, M.; et al. Drug Discov. Today, 2016, 21, 648 ) were used. The drugs classified as "No DILI" ( n = 163) had significantly lower dose and lipophilicity, and higher Fsp3 (fraction of carbon atoms that are sp³ hybridized) versus the "Most DILI" ( n = 163) drugs. The percentages of acids were reduced and bases increased in the "No DILI" versus the "Most DILI" groups. Drugs classified as "Less DILI" or "Ambiguous DILI" had intermediate mean values of dose, lipophilicity, Fsp3, and % acids and bases. The impact of lipophilicity and Fsp3 on DILI increases in the upper 20% versus the lower 80% dose range, and a simple decision tree model predicted "No DILI" versus "Most DILI" outcomes with 82% accuracy. The model correctly classified 19 of 22 drugs (86%) that failed in development due to human hepatotoxicity. Because many oral drugs lacking DILI annotations are predicted to be "Most DILI", the model is best used preclinically in conjunction with experimental DILI mitigation.