Designing around Structural Alerts in Drug Discovery

BCDPi: An interpretable multitask deep neural network model for predicting chemical bioconcentration in fish

Predicting the bioconcentration of chemical compounds plays a crucial role in assessing environmental risks and toxicological impacts. This study presents a robust multitask deep learning model for predicting the bioconcentration potential. The model can predict the bioconcentration of compounds in multiple categories, including non-bioconcentrative (non-BC), weakly bioconcentrative (weak-BC), and strongly bioconcentrative (strong-BC). We also employed the SHapley Additive exPlanations (SHAP) technology for the model interpretation. The binary classification models (non-BC vs BC and weak-BC vs strong-BC) showed good predictive performance, which achieved accuracy values over 90% and area under the curve (AUC) values with 0.95. The final ternary classification model provided an overall accuracy with 91.11%. Comparative analysis of molecular physicochemical properties showed that the importance of molecular weight, polar surface area, solubility, and hydrogen bonding are important for chemical bioconcentration. Besides, we identified eight structural alerts responsible for chemical bioconcentration. We made the model available as an online tool named BCdpi-predictor, which is accessible at http://bcdpi.sapredictor.cn/. Users can predict the bioconcentration potential of chemical compounds freely. The model has significant implications for environmental policy and regulatory frameworks, such as REACH, by providing a more accurate and interpretable method for assessing chemical risks. We hope that the results of this study can provide helpful tools and meaningful information for chemical bioconcentration prediction in environmental risk assessment.

Preferential Synaptic Type of GABA-A Receptor Ligands Enhancing Neuronal Survival and Facilitating Functional Recovery After Ischemic Stroke

Selective enhancement of synaptic GABA signaling mediated by GABA-A receptors has been previously reported to promote functional recovery after ischemic stroke, while tonic GABA signaling has been detrimental. To identify agents that enhance synaptic signaling, we synthesized GABA-A ligands based on three chemotypes with affinity values pKi= 6.44-8.32. Representative compounds showed a preference in functional responses toward synaptic type of GABA-A receptors, compared to the extrasynaptic ones. In a cellular ischemia model (OGD), selected compounds showed the potential to improve neuronal recovery. The selected lead, compound 4, demonstrated the ability to reduce mitochondrial dysfunction, regulate intracellular calcium levels, decrease caspase 3 levels, and promote neurite outgrowth in in vitro assays. In an animal model, compound 4 enhanced motor recovery and showed neuroprotective activity by reducing infarct volume and decreasing poststroke acidosis. These findings underscore the value of selective ligands modulating synaptic GABA-A receptors in promoting recovery from ischemic stroke.

Discovery of PF-07265028, A Selective Small Molecule Inhibitor of Hematopoietic Progenitor Kinase 1 (HPK1) for the Treatment of Cancer

Hematopoietic progenitor kinase 1 (HPK1/MAP4K1) represents a high interest target for the treatment of cancer through an immune-mediated mechanism. Herein we present highlights of the drug discovery campaign within the lactam/azalactam series of inhibitors that yielded a small molecule (21, PF-07265028), which was advanced to a phase 1 clinical trial (NCT05233436). Key components of the discovery effort included optimization of potency through mitigation of ligand strain as guided by the use of cocrystal structures, mitigation of ADME liabilities (plasma instability and fraction metabolism by CYP2D6), and optimization of kinase selectivity, particularly over immune-modulating kinases with high homology to HPK1. Structure-based drug design via leveraging cocrystal structures and lipophilic efficiency analysis proved to be valuable tools that ultimately enabled the delivery of a clinical-quality small molecule inhibitor of HPK1.

In Silico Enabled Discovery of KAI-11101, a Preclinical DLK Inhibitor for the Treatment of Neurodegenerative Disease and Neuronal Injury

Dual leucine zipper kinase (DLK), expressed primarily in neuronal cells, is a regulator of neuronal degeneration in response to cellular stress from chronic disease or neuronal injury. This makes it an attractive target for the treatment of neurodegenerative diseases such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis, and neuronal injury, such as chemotherapy-induced peripheral neuropathy. Here, we describe the discovery of a potent, selective, brain-penetrant DLK inhibitor, KAI-11101 (59). Throughout the program's progression, medicinal chemistry challenges such as potency, hERG inhibition, CNS penetration, CYP3A time-dependent inhibition, and kinase selectivity were overcome through the implementation of cutting-edge in silico tools. KAI-11101 displayed an excellent in vitro safety profile and showed neuroprotective properties in an ex vivo axon fragmentation assay as well as dose-dependent activity in a mouse PD model.

Current Approaches and Strategies Applied in First-in-class Drug Discovery

First-in-class drug discovery (FICDD) offers novel therapies, new biological targets and mechanisms of action (MOAs) toward targeting various diseases and provides opportunities to understand unexplored biology and to target unmet diseases. Current screening approaches followed in FICDD for discovery of hit and lead molecules can be broadly categorized and discussed under phenotypic drug discovery (PDD) and target-based drug discovery (TBDD). Each category has been further classified and described with suitable examples from the literature outlining the current trends in screening approaches applied in small molecule drug discovery (SMDD). Similarly, recent applications of functional genomics, structural biology, artificial intelligence (AI), machine learning (ML), and other such advanced approaches in FICDD have also been highlighted in the article. Further, some of the current medicinal chemistry strategies applied during discovery of hits and optimization studies such as hit-to-lead (HTL) and lead optimization (LO) have been simultaneously overviewed in this article.

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity

Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive toxicity, but none of them make full use of the topological information of compounds. In addition, most existing atom-based graph neural network methods focus on attributing model predictions to individual nodes or edges rather than chemically meaningful fragments or substructures. In current studies, we develop a novel fragment-based graph transformer network (FGTN) approach to generate the QSAR model of human reproductive toxicity by considering internal topological structure information of compounds. In the FGTN model, the compound is represented by a graph architecture using fragments to be nodes and bonds linking two fragments to be edges. A super molecule-level node is further proposed to connect all fragment nodes by undirected edges, obtaining global molecular features from fragment embeddings. The FGTN model achieved an accuracy (ACC) of 0.861 and an area under the receiver operating characteristic curve (AUC) value of 0.914 on nonredundant blind tests, outperforming traditional fingerprint-based machine learning models and atom-based GCN model. The FGTN model can attribute toxic predictions to fragments, generating specific structural alerts for the positive compound. Moreover, FGTN may also have the capability to distinguish various chemical isomers. We believe that FGTN can be used as a reliable and effective tool for human reproductive toxicity prediction in contribution to the advancement of chemical safety assessment.

Discovery of First Branched-Chain Ketoacid Dehydrogenase Kinase (BDK) Inhibitor Clinical Candidate PF-07328948

Inhibition of branched-chain ketoacid dehydrogenase kinase (BDK or BCKDK), a negative regulator of branched-chain amino acid (BCAA) metabolism, is hypothesized to treat cardio-metabolic diseases. From a starting point with potential idiosyncratic toxicity risk, modification to a benzothiophene core and discovery of a cryptic pocket allowed for improved potency with 3-aryl substitution to arrive at PF-07328948, which was largely devoid of protein covalent binding liability. This BDK inhibitor was shown also to be a BDK degrader in cells and in vivo rodent studies. Plasma biomarkers, including BCAAs and branched-chain ketoacids (BCKAs), were lowered in vivo with enhanced pharmacodynamic effect upon chronic dosing due to BDK degradation. This molecule improves metabolic and heart failure end points in rodent models. PF-07328948 is the first known selective BDK inhibitor candidate to be examined in clinical studies, with Phase 1 single ascending dose data showing good tolerability and a pharmacokinetic profile commensurate with once-daily dosing.

Enhancing Aromaticity of Alkenylbenzenes May Decrease Their Metabolic Activation and Reduce Their Potential Cytotoxicity: Lessons Learnt from the Investigation of Myristicin and Elemicin

Metabolic activation studies of lead compounds are a crucial step in drug development and offer a key consideration during rational drug design. Myristicin (MRS) and elemicin (ELM), natural products belonging to alkenylbenzenes, share the backbone of 1-allyl-3-methoxybenzene. The backbone fuses with a methylenedioxy five-membered ring in MRS, while ELM is connected with two adjacent methoxy groups. ELM displayed powerful ability to induce cytotoxicity in cultured primary hepatocytes relative to MRS. Additionally, ELM exhibited superior efficiency in metabolic activation by CYP3A4, resulting in the formation of reactive metabolites carbonium ion, epoxides, and α,β-unsaturated ketone. Quantum chemical calculation and molecular dynamic studies revealed that the fused methylenedioxy 5-membered ring enhances the aromaticity of MRS, which affects the interaction between the allyl side chain and the heme for metabolic activation by the π-π stacking interaction with the aromatic amino acid residues of the host enzyme.

Targeting the Plasmodium falciparum IspE Enzyme

The enzyme IspE in Plasmodium falciparum is considered an attractive drug target, as it is essential for parasite survival and is absent in the human proteome. Yet it still has not been addressed by a small-molecule inhibitor. In this study, we conducted a high-throughput screening campaign against the PfIspE enzyme. Our approach toward a PfIspE inhibitor comprises in vitro screening, structure-activity relationship studies, examining the docking position using an AlphaFold model, and finally target verification through probe binding and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis. The newly synthesized probe containing a diazirine and an alkyne moiety (23) allowed us to demonstrate its binding to IspE in the presence of a lysate of human cells (HEK293 cells) and to get evidence that both probe 23 and the best inhibitor of the series (19) compete for the same IspE binding site.

Hidden potential of hydrazinecarboxamides (semicarbazides) as potential antimicrobial agents: A review

Hydrazinecarboxamides (semicarbazides) are increasingly recognized as a versatile scaffold in developing potential antimicrobial agents. In addition to a brief overview of the synthetic methods to prepare them, this review comprehensively analyses their antimicrobial properties. These derivatives have demonstrated potent activity against a broad spectrum of mycobacteria, bacterial and fungal pathogens, highlighting their potential to address critical human health challenges, including neglected diseases, and to combat growing antimicrobial resistance. They have also been investigated for their antiviral and antiparasitic properties. The review also summarizes structure-activity relationships, known mechanisms of action and emphasizes the crucial role of the hydrazinecarboxamide moiety in facilitating interactions with biological targets. The combination of hydrazinecarboxamides with other bioactive scaffolds (primaquine, isoniazid, etc.) has led to an identification of promising drug candidates, including those active against resistant strains, offering a promising approach for future innovations in the field of antimicrobial therapy. Attention is also drawn to limitations of hydrazinecarboxamides (poor physicochemical properties, cytotoxicity to human cells, and insufficient target selectivity), which may hinder their clinical application.

Convenient syntheses of 2-acylamino-4-halothiazoles and acylated derivatives using a versatile Boc-intermediate

The 2-aminothiazole grouping is a significant feature of many series of biologically active molecules, including antibiotics, anticancer agents and NSAIDs. We have a longstanding interest in the synthesis and biological evaluation of thiazolides, viz. [2-hydroxyaroyl-N-(thiazol-2-yl)-amides] which have broad spectrum antiinfective, especially antiviral, properties. However, 2-amino-4-substituted thiazoles, especially 4-halo examples, are not easily available. We now report practical, efficient syntheses of this class from readily available pseudothiohydantoin, or 2-aminothiazol-4(5H)-one: the key intermediate was its Boc derivative, from which, under Appel-related conditions, Br, Cl and I could all be introduced at C(4). Whereas 2-amino-4-Br/4-Cl thiazoles gave low yields of mixed products on acylation, including a bis-acyl product, further acylation of the Boc intermediates, with a final mild deprotection step, afforded the desired thiazolides cleanly and in good yields. In contrast, even mild hydrolysis of 2-acetamido-4-chlorothiazole led to decomposition with fast reversion to 2-aminothiazol-4(5H)-one. We also present a correction of a claimed synthesis of 2-acetamido-4-chlorothiazole, which in fact produces its 5-chloro isomer.

Synthesis of a Library of Terpenoid Alkaloid-Like Compounds Containing Medium-Sized Rings via Reconstruction of the Humulene Skeleton

The construction of a chemical library based on natural products is a promising method for the synthesis of natural product-like compounds. In this study, we synthesized a terpenoid alkaloid-like compound library based on the humulene skeleton. Our strategy, which enables access to diverse ring systems such as 11-membered monocyclic, oxabicyclic, and medium-sized aza ring-containing scaffolds, involves the introduction of a nitrogen atom, an intermolecular C-O bond formation via Lewis acid-mediated epoxide-opening transannulation, and a ring-reconstruction strategy based on olefin metathesis. A cheminformatics analysis based on their structural and physicochemical properties revealed that the synthesized compounds have high three-dimensionality and high natural product likeness scores but with structural novelty. The usefulness of the terpenoid alkaloid-like compound library for drug discovery and the accessibility to structure-activity relationship studies were validated by performing an assay for osteoclast-specific tartrate-resistant acid phosphatase activity, resulting in the identification of a seed compound for bone-resorptive diseases such as osteoporosis.

Use of Structural Alerts for Reactive Metabolites in the Application SpotRM

Reactive metabolite (RM) formation is widely accepted as playing a crucial role in causing idiosyncratic adverse drug reactions (IADRs), where the liver is most affected. An important goal of drug design is to avoid selection of drug candidates giving rise to RMs and therefore risk causing problems later on involving IADRs. The simplest, initial approach is to avoid test structures that have substructures known or strongly suspected to be associated with IADRs. However, as is evident from the many case reports of IADRs, in most cases a clear association with any (bio)chemical mechanism is lacking, which makes it hard to establish any structure-toxicity relationship. Separate studies of RM formation, in vitro and in vivo, have led to likely evidence and to establishing many structural alerts (SAs) that can be used for fast selection/deselection of planned test compounds. As a background to a discussion of the concept, 25 kinase inhibitor drugs with known problems of hepatotoxicity were probed against a set of SAs contained in the application SpotRM. A clear majority of the probed drugs show liabilities as evident by being flagged by more than one of the fairly established types of SAs. At the same time, no clear SAs were found in three drugs, which is discussed in the broader context of usefulness and selection tactics of SAs in drug design.

Bioactivation and reactivity research advances - 2023 year in review

Advances in the field of bioactivation have significantly contributed to our understanding and prediction of drug-induced liver injury (DILI). It has been established that many adverse drug reactions, including DILI, are associated with the formation and reactivity of metabolites. Modern methods allow us to detect and characterize these reactive metabolites in earlier stages of drug development, which helps anticipate and circumvent the potential for DILI. Improved in silico models and experimental techniques that better reflect in vivo environments are enhancing predictive capabilities for DILI risk. Further, studies on the mechanisms of bioactivation, including enzyme interactions and the role of individual genetic differences, have provided valuable insights for drug optimizations. Cumulatively, this progress is continually refining our approaches to drug safety evaluation and personalized medicine.

Three-dimensional saturated C(sp3)-rich bioisosteres for benzene

Benzenes, the most ubiquitous structural moiety in marketed small-molecule drugs, are frequently associated with poor 'drug-like' properties, including metabolic instability, and poor aqueous solubility. In an effort to overcome these limitations, recent developments in medicinal chemistry have demonstrated the improved physicochemical profiles of C(sp3)-rich bioisosteric scaffolds relative to arenes. In the past two decades, we have witnessed an exponential increase in synthetic methods for accessing saturated bioisosteres of monosubstituted and para-substituted benzenes. However, until recent discoveries, analogous three-dimensional ortho-substituted and meta-substituted biososteres have remained underexplored, owing to their ring strain and increased s-character hybridization. This Review summarizes the emerging synthetic methodologies to access such saturated motifs and their impact on the application of bioisosteres for ortho-substituted, meta-substituted and multi-substituted benzene rings. It concludes with a perspective on the development of next-generation bioisosteres, including those within novel chemical space.

Alpibectir: Early Qualitative and Quantitative Metabolic Profiling from a First-Time-in-Human Study by Combining 19F-NMR (Nuclear Magnetic Resonance), 1H-NMR, and High-Resolution Mass Spectrometric Analyses

Alpibectir (also known as BVL-GSK098 and GSK3729098) is a new chemical entity (NCE) with a novel mechanism for the treatment of tuberculosis. The disposition of alpibectir was determined in subjects from a first-time-in-human trial after a single oral dose of 40 mg and after 7 days repeat dosing at 30 mg. Here we present a combined approach of 19F-NMR (nuclear magnetic resonance), 1H-NMR, and high-resolution mass spectrometry (HRMS) to confidently determine the human metabolic fate of alpibectir. Utilizing multiple sites of fluorination in the molecule, it was possible to fractionate human urine and plasma to confidently detect and quantify the metabolite responses using 19F-NMR. Qualitative detection and structural characterization of F-containing NMR fractions were performed using complementary high-resolution ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) analyses to further add confidence to the metabolite responses in these fractions. Subsequent 1H-NMR then provided unequivocal standard-free structural confirmation for key metabolites, which would not be possible with conventional radioactivity detection and LC-MS/MS techniques. Alpibectir was shown to undergo extensive hydrolysis of the central amide moiety, where the resultant N-dealkylated amine and trifluorobutyric acid products were detected initially by unbiased 19F-NMR detection along with major downstream biotransformations to form a carbamoyl glucuronide conjugate and trifluoroacetic acid, respectively. Parallel UHPLC-MS/MS analyses provided confirmatory or additional structural characterization only where relevant. These concerted data allowed for the qualitative metabolic profile and quantitative determination of drug-related material (DRM) in urine and plasma, along with the percentage of dose excreted in urine, to be reported in a comprehensive, efficient, and data-led manner. SIGNIFICANCE STATEMENT: Combining the selectivity of 19F-NMR (nuclear magnetic resonance) for unfractionated samples as first-intent, data-led sample fractionation prior to 19F-NMR and structure-rich 1H-NMR detection, along with the sensitivity of high-resolution ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS), a novel alternative for time-efficient detection and quantification of drug-related material (DRM) in human without use of radiolabeled drug is reported. This allowed more complete data rationalization of human metabolism, permitting early risk assessment and progression of the development of antitubercular agent, alpibectir.

Development of a fluorous trapping reagent for rapid detection of electrophilic reactive metabolites

A cysteine-based fluorous trapping reagent, Rf8CYS, was developed. Rf8CYS formed adducts with soft and hard electrophilic reactive metabolites. These fluorous-tagged adducts were purified via both fluorous solid-phase extraction and the direct injection method. The highly sensitive mass spectrometric detection of an unprecedented adduct of the ticlopidine metabolite was realized.

Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets

The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate GNN inference on large-scale graphs by jointly pruning the graph structure and the model weights. Though promising, GLT encounters robustness and generalization issues when deployed in real-world scenarios, which are also long-standing and critical problems in deep learning ideology. In real-world scenarios, the distribution of unseen test data is typically diverse. We attribute the failures on out-of-distribution (OOD) data to the incapability of discerning causal patterns, which remain stable amidst distribution shifts. In traditional spase graph learning, the model performance deteriorates dramatically as the graph/network sparsity exceeds a certain high level. Worse still, the pruned GNNs are hard to generalize to unseen graph data due to limited training set at hand. To tackle these issues, we propose the Resilient Graph Lottery Ticket (RGLT) to find more robust and generalizable GLT in GNNs. Concretely, we reactivate a fraction of weights/edges by instantaneous gradient information at each pruning point. After sufficient pruning, we conduct environmental interventions to extrapolate potential test distribution. Finally, we perform last several rounds of model averages to further improve generalization. We provide multiple examples and theoretical analyses that underpin the universality and reliability of our proposal. Further, RGLT has been experimentally verified across various independent identically distributed (IID) and out-of-distribution (OOD) graph benchmarks.

Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.

Small molecule drug metabolite synthesis and identification: why, when and how?

The drug discovery and development process encompasses the interrogation of metabolites arising from the biotransformation of drugs. Here we look at why, when and how metabolites of small-molecule drugs are synthesised from the perspective of a specialist contract research organisation, with particular attention paid to projects for which regulatory oversight is relevant during this journey. To illustrate important aspects, we look at recent case studies, trends and learnings from our experience of making and identifying metabolites over the past ten years, along with with selected examples from the literature.

Novel Isoalantolactone-Based Derivatives as Potent NLRP3 Inflammasome Inhibitors: Design, Synthesis, and Biological Characterization

The NLRP3 inflammasome has been recognized as a promising therapeutic target in drug discovery for inflammatory diseases. Our initial research identified a natural sesquiterpene isoalantolactone (IAL) as the active scaffold targeting NLRP3 inflammasome. To improve its activity and metabolic stability, a total of 64 IAL derivatives were designed and synthesized. Among them, compound 49 emerged as the optimal lead, displaying the most potent inhibitory efficacy on nigericin-induced IL-1β release in THP-1 cells, with an IC50 value of 0.29 μM, approximately 27-fold more potent than that of IAL (IC50: 7.86 μM), and exhibiting higher metabolic stability. Importantly, 49 remarkably improved DSS-induced ulcerative colitis in vivo. Mechanistically, we demonstrated that 49 covalently bound to cysteine 279 in the NACHT domain of NLRP3, thereby inhibiting the assembly and activation of NLRP3 inflammasome. These results provided compelling evidence to further advance the development of more potent NLRP3 inhibitors based on this scaffold.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

Rapid sp3-Enriched Scaffold Generation via a Selective Aziridine Amide Ring-Opening Reaction

Sp3-enriched small molecules play a critical role in developing drug candidates. While designing analogues with greater sp3 character, a methodology utilizing a less explored cyclic-aziridine amide ring-opening reaction to generate sp3-enriched scaffolds has been developed and reported. This methodology enables rapid access to substructures with higher fsp3 values, attracting greater attention within the past few decades. The reaction exhibits a wide reaction scope, featuring a highly sterically hindered phenolic ether, thiophenolic ethers, protected aniline formations, and aliphatic/heteroaromatic ring-containing aziridine amides as substrates. Additionally, this reaction provides access to congested tertiary ether formations through regioselective transformation, applicable to an extensive range of drug discovery targets, construction of complex small molecules, and natural product syntheses. The scaffolds developed show improved physicochemical properties.

Ti-Catalyzed Formal [2π + 2σ] Cycloadditions of Bicyclo[1.1.0]butanes with 2-Azadienes to Access Aminobicyclo[2.1.1]hexanes

Saturated bicyclic amines are increasingly targeted to the pharmaceutical industry as sp3-rich bioisosteres of anilines. Numerous strategies have been established for the preparation of bridgehead aminobicyclics. However, methods to assemble the bridge-amino hydrocarbon skeleton, which serves as a meta-substituted arene bioisostere, are limited. Herein, a general approach to access 2-aminobicyclo[2.1.1]hexanes (aminoBCHs) by titanium-catalyzed formal [2π + 2σ] cycloaddition of bicyclo[1.1.0]butanes and 2-azadienes was developed. Simple derivatization of aminoBCHs leads to various medicinally and agrochemically important analogues.

Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies

During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.

Lead to hit ruthenium-cyclopentadienyl anticancer compounds: Cytotoxicity against breast cancer cells, metabolic stability and metabolite profiling

The successful choice of hit compounds during drug development programs involves the integration of structure-activity relationship (SAR) studies with pharmacokinetic determinations, including metabolic stability assays and metabolite profiling. A panel of nine ruthenium-cyclopentadienyl (RuCp) compounds with the general formula [Ru(η5-C5H4R)(PPh3)(bipyR')]+ (with R = H, CHO, CH2OH; R' = H, CH3, CH2OH, CH2Biotin) has been tested against hormone-dependent MCF-7 and triple negative MDA-MB-231 breast cancer cells. In general, all compounds showed important cytotoxicity against both cancer cell lines and were able to inhibit the formation of MDA-MB-231 colonies in a dose-dependent manner, while showing selectivity for cancer cells over normal fibroblasts. Among them, four compounds stood out as lead structures to be further studied. Cell distribution assays revealed their preference for the accumulation at cell membrane (Ru quantification by ICP-MS) and the mechanism of cell death seemed to be mediated by apoptosis. Potential structural liabilities of lead compounds were subsequently flagged upon in vitro metabolic stability assays and metabolite profiling. The implementation of this integrated strategy led to the selection of RT151 as a promising hit compound.

Deep Learning Algorithm Based on Molecular Fingerprint for Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is one the rare adverse drug reaction (ADR) and multifactorial endpoints. Current preclinical animal models struggle to anticipate it, and in silico methods have emerged as a way with significant potential for doing so. In this study, a high-quality dataset of 1573 compounds was assembled. The 48 classification models, which depended on six different molecular fingerprints, were built via deep neural network (DNN) and seven machine learning algorithms. Comparing the results of the DNN and machine learning models, the optional performing model was found as the one developed based on the DNN with ECFP_6 as input, which achieved the area under the receiver operating characteristic curve (AUC) of 0.713, balanced accuracy (BA) of 0.680, and F1 of 0.753. In addition, we used the SHapley Additive exPlanations (SHAP) algorithm to interpret the models, identified the crucial structural fragments related to DILI risk, and selected the top ten substructures with the highest contribution rankings to serve as warning indicators for subsequent drug hepatotoxicity screening studies. The study demonstrates that the DNN models developed based on molecular fingerprints can be a trustworthy and efficient tool for determining the risk of DILI during the pre-development of novel medications.

Structure-property Relationships Reported for the New Drugs Approved in 2022

BACKGROUND

The structure-property relationship illustrates how modifying the chemical structure of a pharmaceutical compound influences its absorption, distribution, metabolism, excretion, and other related properties. Understanding structure-property relationships of clinically approved drugs could provide useful information for drug design and optimization strategies.

METHOD

Among new drugs approved around the world in 2022, including 37 in the US, structure- property relationships of seven drugs were compiled from medicinal chemistry literature, in which detailed pharmacokinetic and/or physicochemical properties were disclosed not only for the final drug but also for its key analogues generated during drug development.

RESULTS

The discovery campaigns for these seven drugs demonstrate extensive design and optimization efforts to identify suitable candidates for clinical development. Several strategies have been successfully employed, such as attaching a solubilizing group, bioisosteric replacement, and deuterium incorporation, resulting in new compounds with enhanced physicochemical and pharmacokinetic properties.

CONCLUSION

The structure-property relationships hereby summarized illustrate how proper structural modifications could successfully improve the overall drug-like properties. The structure-property relationships of clinically approved drugs are expected to continue to provide valuable references and guides for the development of future drugs.

Atypical N-Alkyl to N-Noralkoxy Switch in a Dual cSRC/BCR-ABL1 Kinase Inhibitor Improves Drug Efflux and hERG Affinity

We describe an atypical amine bioisostere, the trisubstituted hydroxylamine, that upon incorporation into an approved dual cSRC/BCR-ABL1 kinase inhibitor yields 9, a compound that retains potent biological activity and couples it with improved drug efflux and hERG affinity at the expense of only a 2 atomic mass unit increase in molecular weight. Contrary to the common expectation for hydroxylamines in medicinal chemistry, 9 is well tolerated in vivo and lacks the mutagenicity and genotoxicity so often ascribed to lesser substituted hydroxylamines. A matched molecular pair (MMP) analysis suggests that the beneficial properties conferred by the N-alkyl to N-noralkoxy switch arises from a reduction in basicity of the piperazine unit. Overall, these results lend additional support to the use of trisubstituted hydroxylamines as bioisosteres of N-alkyl groups that are not involved in key polar interactions.

Discovery of a Hydroxylamine-Based Brain-Penetrant EGFR Inhibitor for Metastatic Non-Small-Cell Lung Cancer

Metastases to the brain remain a significant problem in lung cancer, as treatment by most small-molecule targeted therapies is severely limited by efflux transporters at the blood-brain barrier (BBB). Here, we report the discovery of a selective, orally bioavailable, epidermal growth factor receptor (EGFR) inhibitor, 9, that exhibits high brain penetration and potent activity in osimertinib-resistant cell lines bearing L858R/C797S and exon19del/C797S EGFR resistance mutations. In vivo, 9 induced tumor regression in an intracranial patient-derived xenograft (PDX) murine model suggesting it as a potential lead for the treatment of localized and metastatic non-small-cell lung cancer (NSCLC) driven by activating mutant bearing EGFR. Overall, we demonstrate that an underrepresented functional group in medicinal chemistry, the trisubstituted hydroxylamine moiety, can be incorporated into a drug scaffold without the toxicity commonly surmised to accompany these units, all while maintaining potent biological activity and without the molecular weight creep common to drug optimization campaigns.

Bioactivation and reactivity research advances - 2022 year in review‡

With the 50th year mark since the launch of Drug Metabolism and Disposition journal, the field of drug metabolism and bioactivation has advanced exponentially in the past decades (Guengerich 2023).This has, in a major part, been due to the continued advances across the whole spectrum of applied technologies in hardware, software, machine learning (ML), and artificial intelligence (AI). LC-MS platforms continue to evolve to support key applications in the field, and automation is also improving the accuracy, precision, and throughput of these supporting assays. In addition, sample generation and processing is being aided by increased diversity and quality of reagents and bio-matrices so that what is being analyzed is more relevant and translatable. The application of in silico platforms (applied software, ML, and AI) is also making great strides, and in tandem with the more traditional approaches mentioned previously, is significantly advancing our understanding of bioactivation pathways and how these play a role in toxicity. All of this continues to allow the area of bioactivation to evolve in parallel with associated fields to help bring novel or improved medicines to patients with urgent or unmet needs.Shuai Wang and Cyrus Khojasteh, on behalf of the authors.

Development of a fluorescent-labeled trapping reagent to evaluate the risk posed by acyl-CoA conjugates

Although acyl-CoA conjugates are known to have higher reactivity than acyl glucuronides, few studies have been conducted to evaluate the risk of the conjugates. In the present study, we aimed to develop a trapping assay for acyl-CoA conjugates using trapping reagents we have developed previously. It was revealed that Cys-Dan, which has both a thiol and an amino group, was the most effective in forming stable adducts containing an amide bond after intramolecular acyl migration. Additionally, we also developed a hepatocyte-based trapping assay in the present study to overcome the shortcomings of liver microsomes. Although liver microsomes are commonly used as enzyme sources in trapping assays, they lack some of the enzymes required for drug metabolism and detoxification systems. In human hepatocytes, our three trapping reagents, CysGlu-Dan, Dap-Dan and Cys-Dan, captured CYP-dependent reactive metabolites, reactive acyl glucuronides, and reactive acyl-CoA conjugates, respectively. The work suggests that the trapping assay with the reagents in hepatocytes is useful to evaluate the risk of reactive metabolites in drug discovery.

Refine and Strengthen SAR-Based Read-Across by Considering Bioactivation and Modes of Action

Structure-activity relationship (SAR)-based read-across is an important and effective method to establish the safety of a data-poor target chemical (structure of interest (SOI)) using hazard data from structurally similar source chemicals (analogues). Many methods use quantitative similarity scores to evaluate the structural similarity for searching and selecting analogues as well as for evaluating analogue suitability. However, studies suggest that read-across based purely on structural similarity cannot accurately predict the toxicity of an SOI. As mechanistic data become available, we gain a greater understanding of the mode of action (MOA), the relationship between structures and metabolism/bioactivation pathways, and the existence of "activity cliffs" in chemical chain length, which can improve the analogue rating process. For this purpose, the current work identifies a series of classes of chemicals where a small change at a key position can result in a significant change in metabolism and bioactivation pathways and may eventually result in significant changes in chemical toxicity that have a big impact on the suitability of analogues for read-across. Additionally, a series of SAR-based read-across case studies are presented, which cover a variety of chemical classes that commonly link to different toxic endpoints. The case study results indicate that SAR-based read-across can be refined and strengthened by considering MOAs or proposed reactive metabolite formation pathways, which can improve the overall accuracy, consistency, transparency, and confidence in evaluating analogue suitability.

High-throughput cytochrome P450 loss and metabolic intermediate complex assays to aid in designing out of CYP3A inactivation

Time-dependent inactivation (TDI) of cytochrome P450 (CYP) enzymes may result in clinical drug-drug interactions (DDIs). Therefore, designing out of CYP TDI prior to advancing a compound to clinical development is highly desirable. As TDI of CYP3A is a common occurrence in small molecule drug discovery, high-throughput methods are sought to help identify the mechanism of inactivation and enable design strategies to mitigate CYP3A TDI. CYP inactivation via modification or destruction of the prosthetic heme group results in loss of the ability of the enzyme to bind carbon monoxide. Additionally, formation of a tight binding complex with the heme iron, referred to as a metabolic intermediate (MI) complex, also results in enzyme inactivation. The methods described herein provide a high-throughput means of identifying and comparing compounds for their ability to inactivate via destruction/modification of the heme via loss of the ability to bind carbon monooxide, as well as via formation of an MI complex.

Structure-Property Optimization of a Series of Imidazopyridines for Visceral Leishmaniasis

Leishmaniasis is a collection of diseases caused by more than 20 Leishmania parasite species that manifest as either visceral, cutaneous, or mucocutaneous leishmaniasis. Despite the significant mortality and morbidity associated with leishmaniasis, it remains a neglected tropical disease. Existing treatments have variable efficacy, significant toxicity, rising resistance, and limited oral bioavailability, which necessitates the development of novel and affordable therapeutics. Here, we report on the continued optimization of a series of imidazopyridines for visceral leishmaniasis and a scaffold hop to a series of substituted 2-(pyridin-2-yl)-6,7-dihydro-5H-pyrrolo[1,2-a]imidazoles with improved absorption, distribution, metabolism, and elimination properties.

An updated patent review of GPR40/ FFAR1 modulators (2020 - present)

INTRODUCTION

Free fatty acid receptor 1 (FFAR1) is a potential therapeutic target for type 2 diabetes mellitus (T2DM) because it could clinically stimulate insulin release in a glucose-dependent manner without inducing hypoglycemia. In both the pharmaceutical industry and academic community, FFAR1 agonists have attracted considerable attention.

AREAS COVERED

The review presents a patent overview of FFAR1 modulators in 2020-2023, along with chemical structures, the biological activities and therapeutic applications of the representative compounds. Our patent survey used the major electronic databases, namely SciFinder, and Web of Science and Innojoy.

EXPERT OPINION

Although FFAR1 agonists exhibit outstanding advantages, they are also associated with significant challenges. At present, reducing the molecular weight and overall lipophilicity and developing tissue-specific FFAR1 agonists may be the strategies for alleviating hepatotoxicity.

Advancing chemical carcinogenicity prediction modeling: opportunities and challenges

Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challenges concerning the inadequacy of available experimentally validated (non)carcinogen datasets and variabilities within bioassays, which contribute to the compromised model training. Existing AI solutions that leverage classical chemistry-driven descriptors do not provide adequate biological interpretability involved in imparting carcinogenicity. This highlights the urgency to devise alternative AI strategies. We propose multiple strategies, including implementing data-driven (integrated databases) and known carcinogen-characteristic-derived features to overcome these apparent shortcomings. In summary, these next-generation approaches will continue facilitating robust chemical carcinogenicity prediction, concomitant with deeper mechanistic insights.

Identifying chemicals based on receptor binding/bioactivation/mechanistic explanation associated with potential to elicit hepatotoxicity and to support structure activity relationship-based read-across

The liver is the most common target organ in toxicology studies. The development of chemical structural alerts for identifying hepatotoxicity will play an important role in in silico model prediction and help strengthen the identification of analogs used in structure activity relationship (SAR)- based read-across. The aim of the current study is development of an SAR-based expert-system decision tree for screening of hepatotoxicants across a wide range of chemistry space and proposed modes of action for clustering of chemicals using defined core chemical categories based on receptor-binding or bioactivation. The decision tree is based on ∼ 1180 different chemicals that were reviewed for hepatotoxicity information. Knowledge of chemical receptor binding, metabolism and mechanistic information were used to group these chemicals into 16 different categories and 102 subcategories: four categories describe binders to 9 different receptors, 11 categories are associated with possible reactive metabolites (RMs) and there is one miscellaneous category. Each chemical subcategory has been associated with possible modes of action (MOAs) or similar key structural features. This decision tree can help to screen potential liver toxicants associated with core structural alerts of receptor binding and/or RMs and be used as a component of weight of evidence decisions based on SAR read-across, and to fill data gaps.

Preclinical evaluation of chemically reactive metabolites and mitigation of bioactivation in drug discovery

The formation of reactive metabolites (RMs) is thought to be one of the pathogeneses for some idiosyncratic adverse drug reactions (IADRs) which are considered one of the leading causes of some drug attritions and/or recalls. Minimizing or eliminating the formation of RMs via chemical modification is a useful tactic to reduce the risk of IADRs and time-dependent inhibition (TDI) of cytochrome P450 enzymes (CYPs). The RMs should be carefully handled before making a go-no-go decision. Herein, we highlight the role of RMs in the occurrence of IADRs and CYP TDI, the risk of structural alerts, the approaches of RM assessment at the discovery stage and strategies to minimize or eliminate RM liability. Finally, some considerations for developing a RM-positive drug candidate are suggested.

Mitigating a Bioactivation Liability with an Azetidine-Based Inhibitor of Diacylglycerol Acyltransferase 2 (DGAT2) En Route to the Discovery of the Clinical Candidate Ervogastat

We recently disclosed SAR studies on systemically acting, amide-based inhibitors of diacylglycerol acyltransferase 2 (DGAT2) that addressed metabolic liabilities with the liver-targeted DGAT2 inhibitor PF-06427878. Despite strategic placement of a nitrogen atom in the dialkoxyaromatic ring in PF-06427878 to evade oxidative O-dearylation, metabolic intrinsic clearance remained high due to extensive piperidine ring oxidation as exemplified with compound 1. Piperidine ring modifications through alternate N-linked heterocyclic ring/spacer combination led to azetidine 2 that demonstrated lower intrinsic clearance. However, 2 underwent a facile cytochrome P450 (CYP)-mediated α-carbon oxidation followed by azetidine ring scission, resulting in the formation of ketone (M2) and aldehyde (M6) as stable metabolites in NADPH-supplemented human liver microsomes. Inclusion of GSH or semicarbazide in microsomal incubations led to the formation of Cys-Gly-thiazolidine (M3), Cys-thiazolidine (M5), and semicarbazone (M7) conjugates, which were derived from reaction of the nucleophilic trapping agents with aldehyde M6. Metabolites M2 and M5 were biosynthesized from NADPH- and l-cysteine-fortified human liver microsomal incubations with 2, and proposed metabolite structures were verified using one- and two-dimensional NMR spectroscopy. Replacement of the azetidine substituent with a pyridine ring furnished 8, which mitigated the formation of the electrophilic aldehyde metabolite, and was a more potent DGAT2 inhibitor than 2. Further structural refinements in 8, specifically introducing amide bond substituents with greater metabolic stability, led to the discovery of PF-06865571 (ervogastat) that is currently in phase 2 clinical trials for the treatment of nonalcoholic steatohepatitis.

Novel Inhibitors of Rickettsia prowazekii Methionine Aminopeptidase from the Malaria Box

Methionine aminopeptidases (MetAp) are dinuclear metalloenzymes found in both prokaryotes and eukaryotes that catalyze the hydrolysis of the N-terminal methionine residue from nascent proteins, an important post-translational modification, which makes it an attractive target for drug discovery. Rickettsia prowazekii (Rp) is an obligate pathogen and causative agent of epidemic typhus and typhus fever. In our ongoing search for anti-rickettsial agents we screened 400 compounds from the Malaria Box for inhibition of RpMetAp1 and discovered 12 compounds that inhibited the enzyme with IC₅₀ values ranging from 800 nM to 22 μM. These inhibitors are from eleven different chemical series and represent leads that can be used to discover more potent and efficacious anti-rickettsial agents.

Unusual Biotransformation Reactions of Drugs and Drug Candidates

Detailed assessment of the fate of drugs in nonclinical test species and humans is essential to ensure the safety and efficacy of medicines in patients. In this context, biotransformation of drugs and drug candidates has been an area of keen interest over many decades in the pharmaceutical industry as well as academia. Although many of the enzymes and biotransformation pathways involved in the metabolism of xenobiotics and more specifically drugs have been well characterized, each drug molecule is unique and constitutes specific challenges for the biotransformation scientist. In this mini-review written for the special issue on the occasion of the 50th Anniversary celebration of Drug Metabolism and Disposition and to celebrate contributions of F. Peter Guengerich, one of the pioneers of the drug metabolism field, recently reported "unusual" biotransformation reactions are presented. Scientific and technological advances in the "toolbox" of the biotransformation scientists are summarized. As the pharmaceutical industry continues to explore therapeutic modalities different from the traditional small molecule drugs, the new challenges confronting the biotransformation scientist as well as future opportunities are discussed. SIGNIFICANCE STATEMENT: For the biotransformation scientists, it is essential to share and be aware of unexpected biotransformation reactions so that they can increase their confidence in predicting metabolites of drugs in humans to ensure the safety and efficacy of these metabolites before the medicines reach large numbers of patients. The purpose of this review is to highlight recent observations of "unusual" metabolites so that the scientists working in the area of drug metabolism can strengthen their readiness in expecting the unexpected.

The pyridazine heterocycle in molecular recognition and drug discovery

The pyridazine ring is endowed with unique physicochemical properties, characterized by weak basicity, a high dipole moment that subtends π-π stacking interactions and robust, dual hydrogen-bonding capacity that can be of importance in drug-target interactions. These properties contribute to unique applications in molecular recognition while the inherent polarity, low cytochrome P450 inhibitory effects and potential to reduce interaction of a molecule with the cardiac hERG potassium channel add additional value in drug discovery and development. The recent approvals of the gonadotropin-releasing hormone receptor antagonist relugolix (24) and the allosteric tyrosine kinase 2 inhibitor deucravacitinib (25) represent the first examples of FDA-approved drugs that incorporate a pyridazine ring. In this review, the properties of the pyridazine ring are summarized in comparison to the other azines and its potential in drug discovery is illustrated through vignettes that explore applications that take advantage of the inherent physicochemical properties as an approach to solving challenges associated with candidate optimization.

Graphical Abstract

Metabolism-Guided Optimization of Tryptophanol-Derived Isoindolinone p53 Activators

For the first time, the pharmacokinetic (PK) profile of tryptophanol-derived isoindolinones, previously reported as p53 activators, was investigated. From the metabolites' identification, performed by liquid chromatography coupled to high resolution tandem mass spectrometry (LC-HRMS/MS), followed by their preparation and structural elucidation, it was possible to identify that the indole C2 and C3 are the main target of the cytochrome P450 (CYP)-promoted oxidative metabolism in the tryptophanol-derived isoindolinone scaffold. Based on these findings, to search for novel p53 activators a series of 16 enantiopure tryptophanol-derived isoindolinones substituted with a bromine in indole C2 was prepared, in yields of 62-89%, and their antiproliferative activity evaluated in human colon adenocarcinoma HCT116 cell lines with and without p53. Structural optimization led to the identification of two (S)-tryptophanol-derived isoindolinones 3.9-fold and 1.9-fold more active than hit SLMP53-1, respectively. Compounds' metabolic stability evaluation revealed that this substitution led to a metabolic switch, with the impact of Phase I oxidative metabolism being minimized. Through differential scanning fluorimetry (DSF) experiments, the most active compound of the series in cell assays led to an increase in the protein melting temperature (Tm) of 10.39 °C, suggesting an effective binding to wild-type p53 core domain.

Rational Design of a New RXR Agonist Scaffold Enabling Single-Subtype Preference for RXRα, RXRβ, and RXRγ

The three retinoid X receptor subtypes (RXRα, RXRβ, RXRγ) exhibit critical regulatory roles in cell proliferation and differentiation, metabolism, and inflammation. Due to their importance in nuclear receptor signaling, RXRs are widely distributed and pan-RXR agonists cause adverse effects, but the three highly conserved RXR ligand binding sites render the development of subtype-selective ligands a major challenge. We have fused elements of known RXR ligands to obtain a new RXR agonist chemotype on which minor structural modifications enabled the development of tools with single-subtype preference for RXRα, RXRβ, and RXRγ. Molecular modeling indicated different binding conformations and interaction patterns with the RXR LBDs as factors of preferential binding. In a phenotypic adipocyte differentiation experiment, only the RXRα preferential tool enhanced the adipogenic effects of pioglitazone, suggesting this subtype as particularly relevant in adipogenesis and highlighting the set of subtype-preferential RXR agonist tools as suitable for functional cellular studies.

Using chemical and biological data to predict drug toxicity

Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, in vitro and in vivo information that can be used to describe compounds and adverse effects. We then explore how compound descriptors based on chemical structure or biological perturbation response can be used to predict safety-related endpoints, and how especially biological data can help us to better understand adverse effects mechanistically. Overall, the described applications demonstrate how large-scale biological information presents new opportunities to anticipate and understand the biological effects of compounds, and how this can support predictive toxicology and drug discovery projects.

Configurational Alteration Results in Change in Hepatotoxicity of Asarone

α-Asarone (αA) and β-asarone (βA) are often used as flavoring agents for alcoholic beverages and food supplements. They possess a double bond in the side chain with different configurations. Double bonds are a class of alert chemical group, due to their metabolic epoxidation to the corresponding epoxides eliciting liver injury. Little is known about changes of configuration on metabolic activation and related toxicity. Here, we report the insight into the mechanisms of hepatotoxicity of asarone with different configurations. In vitro and in vivo comparative studies demonstrated βA displayed higher metabolic activation effectiveness. Apparently, the major metabolic pathway of βA underwent epoxidation at C-1' and C-2', while αA was mainly metabolized to the corresponding alcohol resulting from the hydroxylation of C-3'. CYP1A2 dominated the metabolism of αA and βA. The molecular simulation studies showed that the orientation of βA at the active site of CYP1A2 favored the epoxidation of βA over that of αA. These findings not only remind us that configuration is another important factor for toxicities but also facilitate the understanding of the mechanisms of toxic action of asarone. Additionally, these findings would benefit the risk assessment of αA and βA exposure from foods.

In silico ADMET analysis of the A-, B- and D-modified androstane derivatives with potential anticancer effects

The major challenge in the fight against cancer is to design new drugs that will be more selective for cancer cells, with fewer side effects. Synthetic steroids such as cyproterone, fulvestrant, exemestane and abiraterone are approved powerful drugs for the treatment of hormone-dependent diseases such as breast and prostate cancers. Therefore, androstane derivatives in 17-substituted, 17a-homo lactone and 16,17-seco series, with potent anticancer activity, were selected for pharmacokinetic and druglike predictions from the absorption, distribution, metabolism and excretion (ADME) models. In silico determination of physico-chemical and ADMET properties was performed using SwissADME and ProTox-II web tools. The possibility of gastrointestinal absorption and brain penetration was analyzed using the BOILED-Egg model, while the in silico evaluation of the similarities between selected steroid derivatives and FDA-approved drugs was carried out using the SwissSimilarity tool. Of all tested, two compounds that showed good in silico ADMET results, in addition to promising cytotoxicity and molecular docking results, could potentially be evaluated in in vivo tests.

The discovery of (1R, 3R)-1-(3-chloro-5-fluorophenyl)-3-(hydroxymethyl)-1,2,3,4-tetrahydroisoquinoline-6-carbonitrile, a potent and selective agonist of human transient receptor potential cation channel subfamily m member 5 (TRPM5) and evaluation of as a potential gastrointestinal prokinetic agent

This publication details the discovery of a series of selective transient receptor potential cation channel subfamily M member 5 (TRPM5) agonists culminating with the identification of the lead compound (1R, 3R)-1-(3-chloro-5-fluorophenyl)-3-(hydroxymethyl)-1,2,3,4-tetrahydroisoquinoline-6-carbonitrile (39). We describe herein our biological rationale for agonism of the target, the examination of the then current literature tool molecules, and finally the process of our discovery starting with a high throughput screening hit through lead development. We also detail the selectivity of the lead compound 39 versus related family members TRPA1, TRPV1, TRPV4, TRPM4 and TRPM8, the drug metabolism and pharmacokinetics (DMPK) profile and in vivo efficacy in a mouse model of gastrointestinal motility.

Discovery of Ervogastat (PF-06865571): A Potent and Selective Inhibitor of Diacylglycerol Acyltransferase 2 for the Treatment of Non-alcoholic Steatohepatitis

Discovery efforts leading to the identification of ervogastat (PF-06865571), a systemically acting diacylglycerol acyltransferase (DGAT2) inhibitor that has advanced into clinical trials for the treatment of non-alcoholic steatohepatitis (NASH) with liver fibrosis, are described herein. Ervogastat is a first-in-class DGAT2 inhibitor that addressed potential development risks of the prototype liver-targeted DGAT2 inhibitor PF-06427878. Key design elements that culminated in the discovery of ervogastat are (1) replacement of the metabolically labile motif with a 3,5-disubstituted pyridine system, which addressed potential safety risks arising from a cytochrome P450-mediated O-dearylation of PF-06427878 to a reactive quinone metabolite precursor, and (2) modifications of the amide group to a 3-THF group, guided by metabolite identification studies coupled with property-based drug design.