Abstract: The FDA approval of imatinib in 2001 was a breakthrough in molecularly targeted cancer therapy and heralded the emergence of kinase inhibitors as a key drug class in the oncology area and beyond. Twenty years on, this article analyses the landscape of approved and investigational therapies that target kinases and trends within it, including the most popular targets of kinase inhibitors and their expanding range of indications. There are currently 71 small-molecule kinase inhibitors (SMKIs) approved by the FDA and an additional 16 SMKIs approved by other regulatory agencies. Although oncology is still the predominant area for their application, there have been important approvals for indications such as rheumatoid arthritis, and one-third of the SMKIs in clinical development address disorders beyond oncology. Information on clinical trials of SMKIs reveals that approximately 110 novel kinases are currently being explored as targets, which together with the approximately 45 targets of approved kinase inhibitors represent only about 30% of the human kinome, indicating that there are still substantial unexplored opportunities for this drug class. We also discuss trends in kinase inhibitor design, including the development of allosteric and covalent inhibitors, bifunctional inhibitors and chemical degraders.

Abstract: Novel modalities such as PROTAC and RNAi have the ability to inadvertently alter the abundance of endogenous proteins. Currently available in vitro secondary pharmacology assays, which evaluate off-target binding or activity of small molecules, do not fully assess the off-target effects of PROTAC and are not applicable to RNAi. To address this gap, we developed a proteomics-based platform to comprehensively evaluate the abundance of off-target proteins. First, we selected off-target proteins using genetics and pharmacology evidence. This process yielded 2813 proteins, which we refer to as the "selected off-target proteome" (SOTP). An iterative algorithm was then used to identify four human cell lines out of 932. The 4 cell lines collectively expressed ~ 80% of the SOTP based on transcriptome data. Second, we used mass spectrometry to quantify the intracellular and extracellular proteins from the selected cell lines. Among over 10,000 quantifiable proteins identified, 1828 were part of the predefined SOTP. The SOTP was designed to be easily modified or expanded, owing to the rational selection process developed and the label free LC-MS/MS approach chosen. This versatility inherent to our platform is essential to design fit-for-purpose studies that can address the dynamic questions faced in investigative toxicology.

Abstract: Transcriptomics, proteomics and pathogen-host interactomics data are being explored for the in silico-informed selection of drugs, prior to their functional evaluation. The effectiveness of this kind of strategy has been put to the test in the current COVID-19 pandemic, and it has been paying off, leading to a few drugs being rapidly repurposed as treatment against SARS-CoV-2 infection. Several neglected tropical diseases, for which treatment remains unavailable, would benefit from informed in silico investigations of drugs, as performed in this work for Dengue fever disease. We analyzed transcriptomic data in the key tissues of liver, spleen and blood profiles and verified that despite transcriptomic differences due to tissue specialization, the common mechanisms of action, "Adrenergic receptor antagonist", "ATPase inhibitor", "NF-kB pathway inhibitor" and "Serotonin receptor antagonist", were identified as druggable (e.g., oxprenolol, digoxin, auranofin and palonosetron, respectively) to oppose the effects of severe Dengue infection in these tissues. These are good candidates for future functional evaluation and clinical trials.

Abstract: BACKGROUND: Plasmodium falciparum is an obligate intracellular parasite of humans that causes malaria. Falciparum malaria is a major public health threat to human life responsible for high mortality. Currently, the risk of multi-drug resistance of P. falciparum is rapidly increasing. There is a need to address new anti-malarial therapeutics strategies to combat the drug-resistance threat. METHODS: The P. falciparum essential proteins were retrieved from the recently published studies. These proteins were initially scanned against human host and its gut microbiome proteome sets by comparative proteomics analyses. The human host non-homologs essential proteins of P. falciparum were additionally analysed for druggability potential via in silico methods to possibly identify novel therapeutic targets. Finally, the PfAp4AH target was prioritized for pharmacophore modelling based virtual screening and molecular docking analyses to identify potent inhibitors from drug-like compounds databases. RESULTS: The analyses identified six P. falciparum essential and human host non-homolog proteins that follow the key druggability features. These druggable targets have not been catalogued so far in the Drugbank repository. These prioritized proteins seem novel and promising drug targets against P. falciparum due to their key protein-protein interactions features in pathogen-specific biological pathways and to hold appropriate drug-like molecule binding pockets. The pharmacophore features based virtual screening of Pharmit resource predicted a lead compound i.e. MolPort-045-917-542 as a promising inhibitor of PfAp4AH among prioritized targets. CONCLUSION: The prioritized protein targets may worthy to test in malarial drug discovery programme to overcome the anti-malarial resistance issues. The in-vitro and in-vivo studies might be promising for additional validation of these prioritized lists of drug targets against malaria.

Abstract: After paraquat (PQ) poisoning, it is difficult to accurately diagnose patients' condition by only measuring their blood PQ concentration. Therefore, it is important to establish an accurate method to assist in the diagnosis of PQ poisoning, especially in the early stages. In this study, a gas chromatography-mass spectrometry (GC-MS) metabonomics strategy was established to obtain metabolite information. A random forest algorithm was used to search for potential biomarkers of PQ poisoning, and data mining and network pharmacological analysis were used to evaluate the active components, drug-disease targets, and key pathways of Xuebijing (XBJ) injection in the treatment of PQ-induced pulmonary fibrosis. Targets from the network pharmacology analysis and metabolites from plasma metabolomics were jointly analyzed to select crucial metabolic pathways. Finally, molecular docking technology and in vitro experiments were used to verify the pathway targets to further reveal the potential mechanisms underlying the antipulmonary fibrosis effect of XBJ. Metabonomics studies showed that l-valine, glycine, citric acid, d-mannose, d-galactose, maltose, l-tryptophan, and arachidonic acid contributed more to the differentiation of different groups than other metabolites. Compared with the control group, the PQ poisoning group had higher levels of l-valine, glycine, citric acid, l-tryptophan, and arachidonic acid, and lower levels of d-mannose, d-galactose, and maltose. After treatment with XBJ injection, the relative levels of these metabolites were reversed. The network pharmacological analysis screened a total of 180 targets, mainly involving multiple signaling pathways and metabolic pathways, which jointly played an antipulmonary fibrosis effect. Based on the combined analysis of 180 targets and 8 different metabolites, arachidonic acid metabolism was selected as the key metabolic pathway. Molecular docking analysis showed that the XBJ compound had strong binding activity with the target protein. Western blot results showed that XBJ injection could reduce the inflammatory response by downregulating the expressions of p-p65, p-IKBalpha, and p-IKKbeta, thus inhibiting the development of PQ-induced pulmonary fibrosis. In summary, the combined results from metabolomics and network pharmacology studies showed that Xuebijing has the characteristics of multitarget, multichannel, and multicomponent action in the treatment of pulmonary fibrosis caused by PQ.

Abstract: Importance: Parents of children with severe neurological impairment (SNI) manage complex medication regimens (CMRs) at home, and clinicians can help support parents and simplify CMRs. Objective: To measure the complexity and potentially modifiable aspects of CMRs using the Medication Regimen Complexity Index (MRCI) and to examine the association between MRCI scores and subsequent acute visits. Design, Setting, and Participants: This cross-sectional study was conducted between April 1, 2019, and December 31, 2020, at a single-center, large, hospital-based, complex care clinic. Participants were children with SNI aged 1 to 18 years and 5 or more prescribed medications. Exposure: Home medication regimen complexity was assessed using MRCI scores. The total MRCI score is composed of 3 subscores (dosage form, dose frequency, and specialized instructions). Main Outcomes and Measures: Patient-level counts of subscore characteristics and additional safety variables (total doses per day, high-alert medications, and potential drug-drug interactions) were analyzed by MRCI score groups (low, medium, and high score tertiles). Associations between MRCI score groups and acute visits were tested using Poisson regression, adjusted for age, complex chronic conditions, and recent health care use. Results: Of 123 patients, 73 (59.3%) were male with a median (interquartile range [IQR]) age of 9 (5-13) years. The median (IQR) MRCI scores were 46 (35-61 [range, 8-139]) overall, 29 (24-35) for the low MRCI group, 46 (42-50) for the medium MRCI group, and 69 (61-78) for the high MRCI group. The median (IQR) counts for the subscores were 6 (4-7) dosage forms per patient, 7 (5-9) dose frequencies per patient, and 5 (4-8) instructions per patient, with counts increasing significantly across higher MRCI groups. Similar trends occurred for total daily doses (median [IQR], 31 [20-45] doses), high-alert medications (median [IQR], 3 [1-5] medications), and potential drug-drug interactions (median [IQR], 3 [0-6] interactions). Incidence rate ratios of 30-day acute visits were 1.26 times greater (95% CI, 0.57-2.78) in the medium MRCI group vs the low MRCI group and 2.42 times greater (95% CI, 1.10-5.35) in the high MRCI group vs the low MRCI group. Conclusions and Relevance: Higher MRCI scores were associated with multiple dose frequencies, complicated by different dosage forms and instructions, and associated with subsequent acute visits. These findings suggest that clinical interventions to manage CMRs could target various aspects of these regimens, such as the simplification of dosing schedules.

Abstract: The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the classification of xenobiotics into substrates and non-substrates of nine human CYP isozymes: CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4. The models are trained on an extended, high-quality collection of known substrates and non-substrates and have been subjected to thorough validation. Our results show that the models yield competitive performance and are favorable for the detection of CYP substrates. In particular, a new consensus model reached high performance, with Matthews correlation coefficients (MCCs) between 0.45 (CYP2C8) and 0.85 (CYP3A4), although at the cost of coverage. The best models presented in this work are accessible free of charge via the "CYPstrate" module of the New E-Resource for Drug Discovery (NERDD).

Abstract: The continuous adherence to the conventional "one target, one drug" paradigm has failed so far to provide effective therapeutic solutions for heterogeneous and multifactorial diseases as amyotrophic lateral sclerosis (ALS), a rare progressive and chronic, debilitating neurological disease for which no cure is available. The present study is aimed at finding innovative solutions and paradigms for therapy in ALS pathogenesis, by exploiting new insights from Network Medicine and drug repurposing strategies. To identify new drug-ALS disease associations, we exploited SAveRUNNER, a recently developed network-based algorithm for drug repurposing, which quantifies the proximity of disease-associated genes to drug targets in the human interactome. We prioritized 403 SAveRUNNER-predicted drugs according to decreasing values of network similarity with ALS. Among catecholamine, dopamine, serotonin, histamine, and GABA receptor modulators, as well as angiotensin-converting enzymes, cyclooxygenase isozymes, and serotonin transporter inhibitors, we found some interesting no customary ALS drugs, including amoxapine, clomipramine, mianserin, and modafinil. Furthermore, we strengthened the SAveRUNNER predictions by a gene set enrichment analysis that confirmed modafinil as a drug with the highest score among the 121 identified drugs with a score > 0. Our results contribute to gathering further proofs of innovative solutions for therapy in ALS pathogenesis.

Abstract: The well-defined and characterized 3D crystal structure of a protein is important to explore the topological and physiological features of the protein. The distinguished topography of a protein helps medical chemists design drugs on the basis of the pharmacophoric features of the protein. Structure-based drug discovery, specifically for pathological proteins that cause a higher risk of disease, takes advantage of this fact. Current tools for studying drug-protein interactions include physical, chromatographic, and electrophoretic methods. These techniques can be separated into either non-spectroscopic (equilibrium dialysis, ultrafiltration, ultracentrifugation, etc.) or spectroscopic (Fluorescence spectroscopy, NMR, X-ray diffraction, etc.) methods. These methods, however, can be time-consuming and expensive. On the other hand, in silico methods of analyzing protein-drug interactions, such as docking, molecular simulations, and High-Throughput Virtual Screenings (HTVS), are heavily underutilized by core drug discovery laboratories. These kinds of approaches have a great potential for the mass screening of potential small drugs molecules. Studying protein-drug interactions is of particular importance for understanding how the structural conformation of protein elements affect overall ligand binding affinity. By taking a bioinformatics approach to analyzing drug-protein interactions, the speed with which we identify potential drugs for genetic targets can be greatly increased.

Abstract: Aim: Numerous drugs are being widely prescribed for COVID-19 treatment without any direct evidence for the drug safety/efficacy in patients across diverse ethnic populations. Materials & methods: We analyzed whole genomes of 1029 Indian individuals (IndiGen) to understand the extent of drug-gene (pharmacogenetic), drug-drug and drug-drug-gene interactions associated with COVID-19 therapy in the Indian population. Results: We identified 30 clinically significant pharmacogenetic variants and 73 predicted deleterious pharmacogenetic variants. COVID-19-associated pharmacogenes were substantially overlapped with those of metabolic disorder therapeutics. CYP3A4, ABCB1 and ALB are the most shared pharmacogenes. Fifteen COVID-19 therapeutics were predicted as likely drug-drug interaction candidates when used with four CYP inhibitor drugs. Conclusion: Our findings provide actionable insights for future validation studies and improved clinical decisions for COVID-19 therapy in Indians.