Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors.

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20−25% inhibition of RIPK1's kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.

Revisiting Proteasome Inhibitors: Molecular Underpinnings of Their Development, Mechanisms of Resistance and Strategies to Overcome Anti-Cancer Drug Resistance.

Proteasome inhibitors have shown relevant clinical activity in several hematological malignancies, namely in multiple myeloma and mantle cell lymphoma, improving patient outcomes such as survival and quality of life, when compared with other therapies. However, initial response to the therapy is a challenge as most patients show an innate resistance to proteasome inhibitors, and those that respond to the therapy usually develop late relapses suggesting the development of acquired resistance. The mechanisms of resistance to proteasome inhibition are still controversial and scarce in the literature. In this review, we discuss the development of proteasome inhibitors and the mechanisms of innate and acquired resistance to their activity-a major challenge in preclinical and clinical therapeutics. An improved understanding of these mechanisms is crucial to guiding the design of new and more effective drugs to tackle these devastating diseases. In addition, we provide a comprehensive overview of proteasome inhibitors used in combination with other chemotherapeutic agents, as this is a key strategy to combat resistance.

Exploring EZH2-Proteasome Dual-Targeting Drug Discovery through a Computational Strategy to Fight Multiple Myeloma.

Multiple myeloma is an incurable plasma cell neoplastic disease representing about 10-15% of all haematological malignancies diagnosed in developed countries. Proteasome is a key player in multiple myeloma and proteasome inhibitors are the current first-line of treatment. However, these are associated with limited clinical efficacy due to acquired resistance. One of the solutions to overcome this problem is a polypharmacology approach, namely combination therapy and multitargeting drugs. Several polypharmacology avenues are currently being explored. The simultaneous inhibition of EZH2 and Proteasome 20S remains to be investigated, despite the encouraging evidence of therapeutic synergy between the two. Therefore, we sought to bridge this gap by proposing a holistic in silico strategy to find new dual-target inhibitors. First, we assessed the characteristics of both pockets and compared the chemical space of EZH2 and Proteasome 20S inhibitors, to establish the feasibility of dual targeting. This was followed by molecular docking calculations performed on EZH2 and Proteasome 20S inhibitors from ChEMBL 25, from which we derived a predictive model to propose new EZH2 inhibitors among Proteasome 20S compounds, and vice versa, which yielded two dual-inhibitor hits. Complementarily, we built a machine learning QSAR model for each target but realised their application to our data is very limited as each dataset occupies a different region of chemical space. We finally proceeded with molecular dynamics simulations of the two docking hits against the two targets. Overall, we concluded that one of the hit compounds is particularly promising as a dual-inhibitor candidate exhibiting extensive hydrogen bonding with both targets. Furthermore, this work serves as a framework for how to rationally approach a dual-targeting drug discovery project, from the selection of the targets to the prediction of new hit compounds.

Applying synergy metrics to combination screening data: agreements, disagreements and pitfalls.

Synergistic drug combinations are commonly sought to overcome monotherapy resistance in cancer treatment. To identify such combinations, high-throughput cancer cell line combination screens are performed; and synergy is quantified using competing models based on fundamentally different assumptions. Here, we compare the behaviour of four synergy models, namely Loewe additivity, Bliss independence, highest single agent and zero interaction potency, using the Merck oncology combination screen. We evaluate agreements and disagreements between the models and investigate putative artefacts of each model's assumptions. Despite at least moderate concordance between scores (Pearson's r >0.32, Spearman's ρ>0.34), multiple instances of strong disagreement were observed. Those disagreements are driven by, among others, large differences in tested concentrations, maximum response values and median effective concentrations.

Population Approach to Efavirenz Therapy.

Efavirenz (EFV) is a nonnucleoside reverse transcriptase inhibitor commonly used as first-line therapy in the treatment of human immunodeficiency virus (HIV), with a narrow therapeutic range and a high between-subject variability which can lead to central nervous system toxicity or therapeutic failure. To characterize the sources of variability and better predict EFV steady-state plasma concentrations, a population pharmacokinetic model was developed from 96 HIV-positive individuals, using a nonlinear mixed-effect method with Monolix® software. A one-compartment with first-order absorption and elimination model adequately described the data. To explain between-subject variability, demographic characteristics, biochemical parameters, hepatitis C virus-HIV coinfection, and genetic polymorphisms were tested. A combination of the single-nucleotide polymorphisms rs2279343 and rs3745274, both in the CYP2B6 gene, were the only covariates influencing clearance, included in the final model. Oral clearance was estimated to be 19.6 L/h, 14.15 L/h, and 6.08 L/h for wild-type, heterozygous mutated and homozygous mutated individuals, respectively. These results are in accordance with the current knowledge of EFV metabolism and also suggest that in homozygous mutated individuals, a dose adjustment is necessary. Hepatitis C virus-HIV coinfection does not seem to be a predictive indicator of EFV pharmacokinetic disposition.

Simultaneous Prediction of four ATP-binding Cassette Transporters' Substrates Using Multi-label QSAR.

Efflux by the ATP-binding cassette (ABC) transporters affects the pharmacokinetic profile of drugs and it has been implicated in drug-drug interactions as well as its major role in multi-drug resistance in cancer. It is therefore important for the pharmaceutical industry to be able to understand what phenomena rule ABC substrate recognition. Considering a high degree of substrate overlap between various members of ABC transporter family, it is advantageous to employ a multi-label classification approach where predictions made for one transporter can be used for modeling of the other ABC transporters. Here, we present decision tree-based QSAR classification models able to simultaneously predict substrates and non-substrates for BCRP1, P-gp/MDR1 and MRP1 and MRP2, using a dataset of 1493 compounds. To this end, two multi-label classification QSAR modelling approaches were adopted: Binary Relevance (BR) and Classifier Chain (CC). Even though both multi-label models yielded similar predictive performances in terms of overall accuracies (close to 70 %), the CC model overcame the problem of skewed performance towards identifying substrates compared with non-substrates, which is a common problem in the literature. The models were thoroughly validated by using external testing, applicability domain and activity cliffs characterization. In conclusion, a multi-label classification approach is an appropriate alternative for the prediction of ABC efflux.

Substrate-specific effects of pirinixic acid derivatives on ABCB1-mediated drug transport.

Pirinixic acid derivatives, a new class of drug candidates for a range of diseases, interfere with targets including PPARα, PPARγ, 5-lipoxygenase (5-LO), and microsomal prostaglandin and E2 synthase-1 (mPGES1). Since 5-LO, mPGES1, PPARα, and PPARγ represent potential anti-cancer drug targets, we here investigated the effects of 39 pirinixic acid derivatives on prostate cancer (PC-3) and neuroblastoma (UKF-NB-3) cell viability and, subsequently, the effects of selected compounds on drug-resistant neuroblastoma cells. Few compounds affected cancer cell viability in low micromolar concentrations but there was no correlation between the anti-cancer effects and the effects on 5-LO, mPGES1, PPARα, or PPARγ. Most strikingly, pirinixic acid derivatives interfered with drug transport by the ATP-binding cassette (ABC) transporter ABCB1 in a drug-specific fashion. LP117, the compound that exerted the strongest effect on ABCB1, interfered in the investigated concentrations of up to 2µM with the ABCB1-mediated transport of vincristine, vinorelbine, actinomycin D, paclitaxel, and calcein-AM but not of doxorubicin, rhodamine 123, or JC-1. In silico docking studies identified differences in the interaction profiles of the investigated ABCB1 substrates with the known ABCB1 binding sites that may explain the substrate-specific effects of LP117. Thus, pirinixic acid derivatives may offer potential as drug-specific modulators of ABCB1-mediated drug transport.

The antitumor effects of an arsthinol-cyclodextrin complex in a heterotopic mouse model of glioma.

In this paper, we examined arsthinol-cyclodextrin complexes, which display an anticancer activity. The association constants were 17,502±522 M(-1) for hydroxypropyl-ß-cyclodextrin and 12,038±10,168 M(-1) for randomized methylated ß-cyclodextrin. (1)H NMR experiments in solution also confirmed the formation of these complexes and demonstrated an insertion of the arsthinol (STB) with its dithiarsolane extremity into the wide rim of the hydroxypropyl-ß-cyclodextrin cavity. Complexed arsthinol was more effective than arsenic trioxide (As2O3) and melarsoprol on the U87 MG cell line. Importantly, in the in vivo study, we observed significant antitumor activity against heterotopic xenografts after i.p. administration and did not see any signs of toxicity. This remains to be verified using an orthotopic model.