The use of structural alerts to avoid the toxicity of pharmaceuticals

Computational Docking as a Tool in Guiding the Drug Design of Rutaecarpine Derivatives as Potential SARS-CoV-2 Inhibitors

COVID-19 continues to spread around the world. This is mainly because new variants of the SARS-CoV-2 virus emerge due to genomic mutations, evade the immune system and result in the effectiveness of current therapeutics being reduced. We previously established a series of detection platforms, comprising computational docking analysis, S-protein-based ELISA, pseudovirus entry, and 3CL protease activity assays, which allow us to screen a large library of phytochemicals from natural products and to determine their potential in blocking the entry of SARS-CoV-2. In this new screen, rutaecarpine (an alkaloid from Evodia rutaecarpa) was identified as exhibiting anti-SARS-CoV-2 activity. Therefore, we conducted multiple rounds of structure-activity-relationship (SAR) studies around this phytochemical and generated several rutaecarpine analogs that were subjected to in vitro evaluations. Among these derivatives, RU-75 and RU-184 displayed remarkable inhibitory activity when tested in the 3CL protease assay, S-protein-based ELISA, and pseudovirus entry assay (for both wild-type and omicron variants), and they attenuated the inflammatory response induced by SARS-CoV-2. Interestingly, RU-75 and RU-184 both appeared to be more potent than rutaecarpine itself, and this suggests that they might be considered as lead candidates for future pharmacological elaboration.

Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

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.

Metabolic bioactivation of antidepressants: advance and underlying hepatotoxicity

Many drugs that serve as first-line medications for the treatment of depression are associated with severe side effects, including liver injury. Of the 34 antidepressants discussed in this review, four have been withdrawn from the market due to severe hepatotoxicity, and others carry boxed warnings for idiosyncratic liver toxicity. The clinical and economic implications of antidepressant-induced liver injury are substantial, but the underlying mechanisms remain elusive. Drug-induced liver injury may involve the host immune system, the parent drug, or its metabolites, and reactive drug metabolites are one of the most commonly referenced risk factors. Although the precise mechanism by which toxicity is induced may be difficult to determine, identifying reactive metabolites that cause toxicity can offer valuable insights for decreasing the bioactivation potential of candidates during the drug discovery process. A comprehensive understanding of drug metabolic pathways can mitigate adverse drug-drug interactions that may be caused by elevated formation of reactive metabolites. This review provides a comprehensive overview of the current state of knowledge on antidepressant bioactivation, the metabolizing enzymes responsible for the formation of reactive metabolites, and their potential implication in hepatotoxicity. This information can be a valuable resource for medicinal chemists, toxicologists, and clinicians engaged in the fields of antidepressant development, toxicity, and depression treatment.

Antidiabetic Potential of a Trimeric Anthranilic Acid Peptide Isolated from Malbranchea flocciformis

Compound 3, a trimeric anthranilic acid peptide, and another three metabolites were isolated from an organic extract from the culture medium of Malbranchea flocciformis ATCC 34530. The chemical structure proposed previously for 3 was unequivocally assigned via synthesis and X-ray diffraction analysis. Tripeptide 3 showed insulinotropic properties by decreasing the postprandial peak in healthy and hyperglycemic mice. It also increased glucose-induced insulin secretion in INS-1E at 5 μM, specifically at higher glucose concentrations. These results revealed that 3 might act as an insulin sensitizer and a non-classical insulin secretagogue. Altogether, these findings are in harmony with the in vivo oral glucose tolerance test and acute oral hypoglycemic assay. Finally, the chemical composition of the extract was established by the Global Natural Products Social Molecular Network platform. Phylogenetic analysis using the internal transcribed spacer region revealed that M. flocciformis ATCC 34530 is related to the Malbrancheaceae.

Standardization of zebrafish drug testing parameters for muscle diseases

Skeletal muscular diseases predominantly affect skeletal and cardiac muscle, resulting in muscle weakness, impaired respiratory function and decreased lifespan. These harmful outcomes lead to poor health-related quality of life and carry a high healthcare economic burden. The absence of promising treatments and new therapies for muscular disorders requires new methods for candidate drug identification and advancement in animal models. Consequently, the rapid screening of drug compounds in an animal model that mimics features of human muscle disease is warranted. Zebrafish are a versatile model in preclinical studies that support developmental biology and drug discovery programs for novel chemical entities and repurposing of established drugs. Due to several advantages, there is an increasing number of applications of the zebrafish model for high-throughput drug screening for human disorders and developmental studies. Consequently, standardization of key drug screening parameters, such as animal husbandry protocols, drug compound administration and outcome measures, is paramount for the continued advancement of the model and field. Here, we seek to summarize and explore critical drug treatment and drug screening parameters in the zebrafish-based modeling of human muscle diseases. Through improved standardization and harmonization of drug screening parameters and protocols, we aim to promote more effective drug discovery programs.

Leveraging nitrogen occurrence in approved drugs to identify structural patterns

BACKGROUND

The process of drug development and discovery is costly and slow. Although an understanding of molecular design principles and biochemical processes has progressed, it is essential to minimize synthesis-testing cycles. An effective approach is to analyze key heteroatoms, including oxygen and nitrogen. Herein, we present an analysis focusing on the utilization of nitrogen atoms in approved drugs.

RESEARCH DESIGN AND METHODS

The present work examines the frequency, distribution, prevalence, and diversity of nitrogen atoms in a dataset comprising 2,049 small molecules approved by different regulatory agencies (FDA and others). Various types of nitrogen atoms, such as sp3-, sp2-, sp-hybridized, planar, ring, and non-ring are included in this investigation.

RESULTS

The results unveil both previously reported and newly discovered patterns of nitrogen atom distribution around the center of mass in the majority of drug molecules.

CONCLUSIONS

This study has highlighted intriguing trends in the role of nitrogen atoms in drug design and development. The majority of drugs contain 1-3 nitrogen atoms within 5Å from the center of mass (COM) of a molecule, with a higher preference for the ring and planar nitrogen atoms. The results offer invaluable guidance for the multiparameter optimization process, thus significantly contributing toward the conversion of lead compounds into potential drug candidates.

Synthesis and initial in vitro evaluation of olmutinib derivatives as prospective imaging probe for non-small cell lung cancer

Introduction

Imaging a non-small cell lung cancer (NSCLC) using radiolabeled tyrosine kinase inhibitors (TKIs) has attracted attention due to their unique interaction with the target epidermal growth factor receptor (EGFR). Olmutinib (OTB) is one of the third-generation EGFR TKIs, which selectively inhibit EGFR L858R/T790M mutation. In this study, we aim to estimate the interaction of the iodinated OTB (I-OTB)-receptor complex by molecular docking. Furthermore, we will synthesize the I-OTB and evaluate its activity toward EGFR L858R/T790M by in vitro cytotoxicity assay.

Methods

A molecular docking simulation was carried out using an AutoDock Vina program package to estimate the interaction of the ligand-receptor complex. The I-OTB, N-{3-iodo-5-[(2-{[4-(4-methylpiperazin-1-yl)phenyl]aminothieno{3,2-d}pyrimidin-4-yl)oxy]phenyl} acrylamide, was synthesized by introducing an iodine atom in the phenyl group in the 3-aryloxyanilide structure. The half inhibitory concentration (IC50) was determined by employing a 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H tetrazolium monosodium salt (WST-8) assay to evaluate the activity of I-OTB.

Results

The docking study exhibited that I-OTB could take an interaction similar to that of the parent compound. We successfully synthesized I-OTB and confirmed its structure by instrumental analysis. The binding energy of OTB and I-OTB in complex with EGFR T790M are -8.7 and -7.9 kcal/mol, respectively. The cytotoxicity assay showed that I-OTB also has an affinity towards the EGFR L858R/T790M mutation with the IC50 10.49 ± 5.64 𝜇M compared to the EGFR wild type with the IC50 over than 10 𝜇M.

Conclusion

The cytotoxicity effect of I-OTB was comparable to that of OTB. This result indicates that the iodine substituent in OTB did not alter the parent compound selectivity toward double mutations EGFR L858R/T790M. Therefore, I-OTB is prominent for radioiodination, and [123/124I] I-OTB may be a promising candidate for EGFR L858R/T790M mutation imaging.

Integrated data-driven cross-disciplinary framework to prevent chemical water pollution

Access to a clean and healthy environment is a human right and a prerequisite for maintaining a sustainable ecosystem. Experts across domains along the chemical life cycle have traditionally operated in isolation, leading to limited connectivity between upstream chemical innovation to downstream development of water-treatment technologies. This fragmented and historically reactive approach to managing emerging contaminants has resulted in significant externalized societal costs. Herein, we propose an integrated data-driven framework to foster proactive action across domains to effectively address chemical water pollution. By implementing this integrated framework, it will not only enhance the capabilities of experts in their respective fields but also create opportunities for novel approaches that yield co-benefits across multiple domains. To successfully operationalize the integrated framework, several concerted efforts are warranted, including adopting open and FAIR (findable, accessible, interoperable, and reusable) data practices, developing common knowledge bases/platforms, and staying vigilant against new substance "properties" of concern.

Novel 1,2,4-oxadiazole compounds as PPAR-α ligand agonists: a new strategy for the design of antitumour compounds

Modulation of PPAR-α by natural ligands is a novel strategy for the development of anticancer therapies. A series of 16 compounds based on the structure of 3-(pyridin-3-yl)-5-(thiophen-3-yl)-1,2,4-oxadiazole (natural compound) with antitumour potential were designed and synthesised. The cytotoxicity and PPAR agonist activity of these synthetic 1,2,4-oxadiazoles were evaluated in the A-498 and DU 145 tumour cell lines. Preliminary biological evaluation showed that most of these synthetic 1,2,4-oxadiazoles are less cytotoxic (sulforhodamine B assay) than the positive control WY-14643. Regarding the PPAR-α modulation, compound 16 was the most active, with EC50 = 0.23-0.83 μM (PPAR-α). Additionally, compound 16 had a similar activity to the natural compound (EC50 = 0.18-0.77 μM) and was less toxic in the RPTEC and WPMY-1 cell lines (non-tumour cells) (CC50 = 81.66-92.67 μM) than the natural compound. Looking at the link between chemical structure and activity, our study demonstrates that changes to the natural 1,2,4-oxadiazole at the level of the thiophenyl residue can lead to new agonists of PPAR-α with promising anti-tumour activity.

Message Passing Neural Networks Improve Prediction of Metabolite Authenticity

Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely react with protein and DNA and prompt drug candidate attrition or market withdrawal. Previously developed models help understand how these enzymes modify molecule structure by predicting sites of metabolism or characterizing formation of metabolite-biomolecule adducts. However, the majority of reactive metabolites are formed by multiple metabolic steps, and understanding the progenitor molecule's network-level behavior necessitates an integrative approach that blends multiple site of metabolism and structure inference models. Our previously developed tool, XenoNet 1.0, generates metabolic networks, where nodes are molecules and weighted edges are metabolic transformations. We extend XenoNet with a bidirectional message passing neural network that integrates edge feature information and local network structure using edge-conditioned graph convolutions and jumping knowledge to predict the authenticity of inferred Phase I metabolite structures. Our model significantly outperformed prior work and algorithmic baselines on a data set of 311 networks and 6606 intermediates annotated using a chemically diverse set of 20 736 individual in vitro and in vivo reaction records accounting for 92.3% of all human Phase I metabolism in the Accelrys Metabolite Database. Cross-validated predictions resulted in area under the receiver operating characteristic curves of 88.5% and 87.6% for separating experimentally observed and unobserved metabolites at global and network levels, respectively. Further analysis verified robustness to networks of varying depth and breadth, accurate detection of metabolites, such as d,l-methamphetamine, that are experimentally observed or unobserved in different network contexts, extraction of important metabolic subnetworks, and identification of known bioactivation pathways, such as for nimesulide and terbinafine. By exploiting network structures, our approach accurately suggests unreported metabolites for experimental study and may rationalize modifications for avoiding deleterious pathways antecedent to reactive metabolite formation.

2-(4-Fluorophenyl)-1H-benzo[d]imidazole as a Promising Template for the Development of Metabolically Robust, α1β2γ2GABA-A Receptor-Positive Allosteric Modulators

Modulation of α1β2γ2GABA-A receptor subpopulation expressed in the basal ganglia region is a conceptually novel mode of pharmacological strategy that offers prospects to tackle a variety of neurological dysfunction. Although clinical findings provided compelling evidence for the validity of this strategy, the current chemical space of molecules able to modulate the α1/γ2 interface of the GABA-A receptor is limited to imidazo[1,2-a]pyridine derivatives that undergo rapid biotransformation. In response to a deficiency in the chemical repertoire of GABA-A receptors, we identified a series of 2-(4-fluorophenyl)-1H-benzo[d]imidazoles as positive allosteric modulators (PAMs) with improved metabolic stability and reduced potential for hepatotoxicity, where lead molecules 9 and 23 displayed interesting features in a preliminary investigation. We further disclose that the identified scaffold shows a preference for interaction with the α1/γ2 interface of the GABA-A receptor, delivering several PAMs of the GABA-A receptor. The present work provides useful chemical templates to further explore the therapeutic potential of GABA-A receptor ligands and enriches the chemical space of molecules suitable for the interaction with the α1/γ2 interface.

Antiparasitic Meroterpenoids Isolated from Memnoniella dichroa CF-080171

Memnoniella is a fungal genus from which a wide range of diverse biologically active compounds have been isolated. A Memnoniella dichroa CF-080171 extract was identified to exhibit potent activity against Plasmodium falciparum 3D7 and Trypanosoma cruzi Tulahuen whole parasites in a high-throughput screening (HTS) campaign of microbial extracts from the Fundación MEDINA's collection. Bioassay-guided isolation of the active metabolites from this extract afforded eight new meroterpenoids of varying potencies, namely, memnobotrins C-E (1-3), a glycosylated isobenzofuranone (4), a tricyclic isobenzofuranone (5), a tetracyclic benzopyrane (6), a tetracyclic isobenzofuranone (7), and a pentacyclic isobenzofuranone (8). The structures of the isolated compounds were established by (+)-ESI-TOF high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy. Compounds 1, 2, and 4 exhibited potent antiparasitic activity against P. falciparum 3D7 (EC₅₀ 0.04-0.243 μM) and T. cruzi Tulahuen (EC₅₀ 0.266-1.37 μM) parasites, as well as cytotoxic activity against HepG2 tumoral liver cells (EC₅₀ 1.20-4.84 μM). The remaining compounds (3, 5-8) showed moderate or no activity against the above-mentioned parasites and cells.

In vitro metabolic characterization of the SARS-CoV-2 papain-like protease inhibitors GRL0617 and HY-17542

The SARS-CoV-2 pandemic requires a new therapeutic target for viral infection, and papain-like protease (Plpro) has been suggested as a druggable target. This in-vitro study was conducted to examine the drug metabolism of the GRL0617 and HY-17542, Plpro inhibitors. Metabolism of these inhibitors was studied to predict the pharmacokinetics in human liver microsomes. The hepatic cytochrome P450 (CYP) isoforms responsible for their metabolism were identified using recombinant enzymes. The drug-drug interaction potential mediated by cytochrome P450 inhibition was estimated. In human liver microsomes, the Plpro inhibitors had phase I and phase I + II metabolism with half-lives of 26.35 and 29.53 min, respectively. Hydroxylation (M1) and desaturation (-H2, M3) of the para-amino toluene side chain were the predominant reactions mediated with CYP3A4 and CYP3A5. CYP2D6 is responsible for the hydroxylation of the naphthalene side ring. GRL0617 inhibits major drug-metabolizing enzymes, including CYP2C9 and CYP3A4. HY-17542 is structural analog of GRL0617 and it is metabolized to GRL0617 through non-cytochrome P450 reactions in human liver microsomes without NADPH. Like GRL0617 and HY-17542 undergoes additional hepatic metabolism. The in-vitro hepatic metabolism of the Plpro inhibitors featured short half-lives; preclinical metabolism studies are needed to determine therapeutic doses for these inhibitors.

Applications of In Silico Models to Predict Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of the withdrawal of pre-marketed drugs, typically attributed to oxidative stress, mitochondrial damage, disrupted bile acid homeostasis, and innate immune-related inflammation. DILI can be divided into intrinsic and idiosyncratic DILI with cholestatic liver injury as an important manifestation. The diagnosis of DILI remains a challenge today and relies on clinical judgment and knowledge of the insulting agent. Early prediction of hepatotoxicity is an important but still unfulfilled component of drug development. In response, in silico modeling has shown good potential to fill the missing puzzle. Computer algorithms, with machine learning and artificial intelligence as a representative, can be established to initiate a reaction on the given condition to predict DILI. DILIsym is a mechanistic approach that integrates physiologically based pharmacokinetic modeling with the mechanisms of hepatoxicity and has gained increasing popularity for DILI prediction. This article reviews existing in silico approaches utilized to predict DILI risks in clinical medication and provides an overview of the underlying principles and related practical applications.

LC/Q-TOF MS and LC/QQQ MS based bioanalysis of a new ferrocene derivative as a potential anticancer lead with promising drug-like characteristics

Pyrazolopyrimidine ring present in various approved drugs is reported to target the tyrosine kinase receptor. A new pyrazolopyrimidine ferrocene derivative, which targets tumor pyruvate kinase M2 showed an impressive antiproliferative profile against human oral squamous cell carcinoma cell line CAL27 assessed using Alamar blue assay. In line with the lead optimization process, the molecule was studied for physicochemical properties where a bioanalytical method has been developed in plasma on liquid chromatography-mass spectrometry and validated following the USFDA bioanalytical method validation guideline. Plasma stability and plasma protein binding potential of the molecule have been evaluated. All the major metabolites of the compound have been identified through in vitro metabolite study employing rat liver microsome, human liver microsome, and human S9 fractions. The in silico toxicity profile of the metabolites was assessed using ProTox II software. Log P, Log D, and pKa of the molecule were found to be 4.5, 5, and 12, respectively. The molecule was found to be quite stable in plasma and have a moderate affinity towards plasma proteins (about 75 % binding). Four major metabolites have been identified and characterized by UHPLCQ-TOF-MS. The metabolites were found to have a moderate safety profile. The validated bioanalytical method and the metabolic pathway will be useful for future clinical studies and to assess the safety profile of the molecule. The finding of this study may also be useful in analyzing the desired drug-like properties through bioanalysis while designing new chemical entities based on metallocenes.

Modeling and insights into the structural basis of chemical acute aquatic toxicity

It has become a top global regulatory priority to prevent and control pollution from the release of synthetic chemicals, which continues to affect the aquatic communities. In the past decades, computational tools were largely used to significantly reduce the budget and time cost of chemical acute aquatic toxicity assessment. But the structural basis of toxic compounds was rarely analyzed. In the present study, we collected 1438, 485 and 961 chemicals with acute toxicity data records for three representative aquatic species, including Tetrahymena pyriformis, Daphnia magna, and Fathead minnow, respectively. A series of artificial intelligence models were developed using OCHEM tools. For each aquatic toxicity endpoint, a consensus model was developed based on the top performed individual models. The consensus models provided good performance on external validation sets with total accuracy values 96.88 %, 90.63 %, and 84.90 % for Tetrahymena pyriformis toxicity (TPT), Daphnia magna toxicity (DMT), and Fathead minnow toxicity (FMT), respectively. The models can be freely accessed via https://ochem.eu/article/146910. Moreover, the analysis of physical-chemical properties suggested that several key molecular properties of aquatic toxic compounds were significantly different with those of non-toxic compounds. Thus, these descriptors may be associated to chemical acute aquatic toxicity, and may be useful for the understand of chemical aquatic toxicity. Besides, in this study, the structural alerts for aquatic toxicity were detected using f-score and frequency ratio analysis of predefined substructures. A total of 112, 58 and 33 structural alerts were identified responsible for TPT, DMT, and FMT, respectively. These structural alerts could provide useful information for the mechanisms of chemical aquatic toxicity and visual alerts for environmental assessment. All the structural alerts were integrated in the web-server SApredictor (www.sapredictor.cn).

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts

The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be “black box”, which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.

Modifications of the Triaminoaryl Metabophore of Flupirtine and Retigabine Aimed at Avoiding Quinone Diimine Formation

The potassium channel opening drugs flupirtine and retigabine have been withdrawn from the market due to occasional drug-induced liver injury (DILI) and tissue discoloration, respectively. While the mechanism underlying DILI after prolonged flupirtine use is not entirely understood, evidence indicates that both drugs are metabolized in an initial step to reactive ortho- and/or para-azaquinone diimines or ortho- and/or para-quinone diimines, respectively. Aiming to develop safer alternatives for the treatment of pain and epilepsy, we have attempted to separate activity from toxicity by employing a drug design strategy of avoiding the detrimental oxidation of the central aromatic ring by shifting oxidation toward the formation of benign metabolites. In the present investigation, an alternative retrometabolic design strategy was followed. The nitrogen atom, which could be involved in the formation of both ortho- or para-quinone diimines of the lead structures, was shifted away from the central ring, yielding a substitution pattern with nitrogen substituents in the meta position only. Evaluation of K_V7.2/3 opening activity of the 11 new specially designed derivatives revealed surprisingly steep structure-activity relationship data with inactive compounds and an activity cliff that led to the identification of an apparent "magic methyl" effect in the case of N-(4-fluorobenzyl)-6-[(4-fluorobenzyl)amino]-2-methoxy-4-methylnicotinamide. This flupirtine analogue showed potent K_V7.2/3 opening activity, being six times as active as flupirtine itself, and by design is devoid of the potential for azaquinone diimine formation.

Molecular Engineering of Peptide–Drug Conjugates for Therapeutics

In recent years, hundreds of novel small molecular drugs used for different treatments have been studied in the three phases of clinical trials around the world. However, less than 10% of them are eventually used due to diverse problems. Even some traditional drugs that have been approved by the Food and Drug Administration (FDA) have faced similar dilemmas. For instance, many drugs have poor water solubility, are easily hydrolyzed, or possess undesirable toxicity, while a variety of cancer cells develop drug resistance (DR) or multiple drug resistance (MDR) towards chemotherapeutic agents after long-term therapy. In order to improve the efficacy and efficiency of drugs, research has been directed forward towards the creation of assemblies of peptide–drug conjugates (PDCs) which have proven to possess wide potential for overcoming such complications based on their excellent biocompatibility, controllable biodegradability, site-selective targeting, and comparably low cytotoxicity. In this review, we focus on the recent developments and advances made in the creation of self-assembled nanostructures of PDCs for cancer therapy, on the chemical and physical properties of such drugs and peptides, and how they are arranged together to form diverse supramolecular nanostructures. Additionally, we cover certain mechanisms regarding how peptides or their derivatives enhance the efficiency and efficacy of those selected drugs and provide a brief discussion regarding the perspectives and remaining challenges in this intriguing field.

Reactive metabolites of the anticonvulsant drugs and approaches to minimize the adverse drug reaction

Several generations of antiepileptic drugs (AEDs) are available in the market for the treatment of seizures, but these are amalgamated with acute to chronic side effects. The most common side effects of AEDs are dose-related, but some are idiosyncratic adverse drug reactions (ADRs) that transpire due to the formation of reactive metabolite (RM) after the bioactivation process. Because of the adverse reactions patients usually discontinue the medication in between the treatment. The AEDs such as valproic acid, lamotrigine, phenytoin etc., can be categorized under such types because they form the RM which may prevail with life-threatening adverse effects or immune-mediated reactions. Hepatotoxicity, teratogenicity, cutaneous hypersensitivity, dizziness, addiction, serum sickness reaction, renal calculi, metabolic acidosis are associated with the metabolites of drugs such as arene oxide, N-desmethyldiazepam, 2-(1-hydroxyethyl)-2-methylsuccinimide, 2-(sulphamoy1acetyl)-phenol, E-2-en-VPA and 4-en-VPA and carbamazepine-10,11-epoxide, etc. The major toxicities are associated with the moieties that are either capable of forming RM or the functional groups may itself be too reactive prior to the metabolism. These functional groups or fragment structures are typically known as structural alerts or toxicophores. Therefore, minimizing the bioactivation potential of lead structures in the early phases of drug discovery by a modification to low-risk drug molecules is a priority for the pharmaceutical companies. Additionally, excellent potency and pharmacokinetic (PK) behaviour help in ensuring that appropriate (low dose) candidate drugs progress into the development phase. The current review discusses about RMs in the anticonvulsant drugs along with their mechanism vis-a-vis research efforts that have been taken to minimize the toxic effects of AEDs therapy.

Screening of 6000 Compounds for Uncoupling Activity: A Comparison Between a Mechanistic Biophysical Model and the Structural Alert Profiler Mitotox

Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes, or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in vitro uncoupling activity, using a biophysical model based on ab initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural alert profiler Mitotox (97%), but much higher sensitivity than Mitotox (47%). The biophysical model thus allows for a more accurate screening for uncoupling activity than existing structural alert profilers. We propose to use our model as a complementary tool to screen large datasets for protonophoric uncoupling activity in drug development and toxicity assessment.

Finding the First Potential Inhibitors of Shikimate Kinase from Methicillin Resistant Staphylococcus aureus through Computer-Assisted Drug Design

Methicillin-resistant Staphylococcus aureus (MRSA) is an important threat as it causes serious hospital and community acquired infections with deathly outcomes oftentimes, therefore, development of new treatments against this bacterium is a priority. Shikimate kinase, an enzyme in the shikimate pathway, is considered a good target for developing antimicrobial drugs; this is given because of its pathway, which is essential in bacteria whereas it is absent in mammals. In this work, a computer-assisted drug design strategy was used to report the first potentials inhibitors for Shikimate kinase from methicillin-resistant Staphylococcus aureus (SaSK), employing approximately 5 million compounds from ZINC15 database. Diverse filtering criteria, related to druglike characteristics and virtual docking screening in the shikimate binding site, were performed to select structurally diverse potential inhibitors from SaSK. Molecular dynamics simulations were performed to elucidate the dynamic behavior of each SaSK–ligand complex. The potential inhibitors formed important interactions with residues that are crucial for enzyme catalysis, such as Asp37, Arg61, Gly82, and Arg138. Therefore, the compounds reported provide valuable information and can be seen as the first step toward developing SaSK inhibitors in the search of new drugs against MRSA.

Hair analysis for New Psychoactive Substances (NPS): Still far from becoming the tool to study NPS spread in the community?

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The detection of 280 NPS has been reported to be enabled through hair analysis.

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The LODs/ LOQs for these NPS are as low as pg/mg of hair.

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The NPS hair concentrations in clinical/forensic samples are considerably higher than the respective LOD.

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Untargeted-mass spectroscopic detection techniques could advance NPS hair analysis.

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NPS hair analysis could become the tool to monitor the extent of NPS use worldwide.

In this review article, we performed an overview of extraction and chromatographic analysis methods of NPS in hair from 2007 to 2021, evaluating the limit of detection (LOD), limit of quantification (LOQ), limit of reporting (LOR), and limit of identification (LOI) values reported for each NPS. Our review aimed to highlight the limitations of modern hair analytical techniques, and the prerequisites for the proper evaluation and use of analytical results in relation to the objectives of NPS hair analysis. In the selected studies the detection of a total of 280 NPS was reported. The detected NPS belonged to seven classes: synthetic cannabinoids with 109 different substances, synthetic opioids with 58, cathinones with 50, phenethylamines with 34, other NPS with 15, tryptamines with ten, and piperazines with four substances. The NPS hair analysis of real forensic/ clinical cases reported the detection of only 80 NPS (out of the 280 targeted), in significantly higher levels than the respective LODs. The analytical protocols reviewed herein for NPS hair analysis showed continuously growing trends to identify as many NPS as possible; the extraction methods seem to have a limited potential to improve, while the various mass spectroscopic techniques and relevant instrumentation provide an enormous field for development and application. Hair is a biological indicator of the past chronic, sub-chronic, and, even, in certain cases, acute exposure to xenobiotics. Therefore, future research in the field could progress NPS hair analysis and aim the monitoring of NPS expansion and extent of use in the community.

Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose

An effective approach for assessing a drug’s potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD. We constructed a database containing 171 RM-related structural alerts, generated a dataset of 407 AD- and non-AD-associated drugs, and performed statistical analysis. The nitrogen-containing benzene substituent alerts were found to be significantly associated with the risk of drug-induced ADs (odds ratio = 2.95, p = 0.0036). Furthermore, we developed a machine-learning-based predictive model by using daily dose and nitrogen-containing benzene substituent alerts as the top inputs and achieved the predictive performance of area under curve (AUC) of 70%. Additionally, we confirmed the reactivity of the nitrogen-containing benzene substituent aniline and related metabolites using quantum chemistry analysis and explored the underlying mechanisms. These identified structural alerts could be helpful in identifying drug candidates that carry a potential risk of drug-induced ADs to improve their safety profiles.

In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities

BACKGROUND

Drug-induced toxicity is one of the problems that have negatively impacted on the well-being of populations throughout the world, including Malawi. It results in unnecessary hospitalizations, retarding the development of the country. This study assessed the Malawi Essential Medicines List (MEML) for structural alerts and reactive metabolites with the potential for drug-induced toxicities.

METHODS

This in-silico screening study used StopTox, ToxAlerts and LD-50 values toxicity models to assess the MEML drugs. A total of 296 drugs qualified for the analysis (those that had defined chemical structures) and were screened in each software programme. Each model had its own toxicity endpoints and the models were compared for consensus of their results.

RESULTS

In the StopTox model, 86% of the drugs had potential to cause at least one toxicity including 55% that had the potential of causing eye irritation and corrosion. In ToxAlerts, 90% of the drugs had the potential of causing at least one toxicity and 72% were found to be potentially reactive, unstable and toxic. In LD-50, 70% of the drugs were potentially toxic. Model consensus evaluation results showed that the highest consensus was observed between ToxAlerts and StopTox (80%). The overall consensus amongst the three models was 57% and statistically significant (p < 0.05).

CONCLUSIONS

A large number of drugs had the potential to cause various systemic toxicities. But the results need to be interpreted cautiously since the clinical translation of QSAR-based predictions depends on many factors. In addition, inconsistencies have been reported between screening results amongst different models.

SANCDB: an update on South African natural compounds and their readily available analogs

BACKGROUND

South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/ ) is the sole and a fully referenced database of natural chemical compounds of South African biodiversity. It is freely available, and since its inception in 2015, the database has become an important resource to several studies. Its content has been: used as training data for machine learning models; incorporated to larger databases; and utilized in drug discovery studies for hit identifications.

DESCRIPTION

Here, we report the updated version of SANCDB. The new version includes 412 additional compounds that have been reported since 2015, giving a total of 1012 compounds in the database. Further, although natural products (NPs) are an important source of unique scaffolds, they have a major drawback due to their complex structure resulting in low synthetic feasibility in the laboratory. With this in mind, SANCDB is, now, updated to provide direct links to commercially available analogs from two major chemical databases namely Mcule and MolPort. To our knowledge, this feature is not available in other NP databases. Additionally, for easier access to information by users, the database and website interface were updated. The compounds are now downloadable in many different chemical formats.

CONCLUSIONS

The drug discovery process relies heavily on NPs due to their unique chemical organization. This has inspired the establishment of numerous NP chemical databases. With the emergence of newer chemoinformatic technologies, existing chemical databases require constant updates to facilitate information accessibility and integration by users. Besides increasing the NPs compound content, the updated SANCDB allows users to access the individual compounds (if available) or their analogs from commercial databases seamlessly.

Further lead optimization on Bax activators: Design, synthesis and pharmacological evaluation of 2-fluoro-fluorene derivatives for the treatment of breast cancer

To further pursue potent Bax activators with better safety profiles for the treatment of breast cancer, structural optimization was conducted based on lead compound CYD-4-61 through several strategies, including scaffold hopping on the 2-nitro-fluorene ring, replacement of the nitro group with bioisosteres to avoid potential toxicity, and further optimization on the upper pyridine by exploring diverse alkylamine linkers as a tail or replacing the pyridine with bioisosteric heterocycles. F-containing compound 22d (GL0388) exhibited a good balance between the activity and toxicity, displaying submicromolar activities against a variety of cancer cell lines with 5.8-10.7-fold selectivity of decreased activity to MCF-10A human mammary epithelial cell line. Compound 22d dose-dependently blocked colony formation of breast cancer cells and prevented the migration and invasion of MDA-MB-231 cells. Mechanism of action studies indicate that 22d activated Bax, rendering its insertion into mitochondrial membrane, thereby leading to cytochrome c release from the mitochondria into the cytoplasm, subsequently inducing release of apoptotic biomarkers. Further in vivo efficacy studies of 22d in human breast cancer xenografts arisen from MDA-MB-231 cells demonstrated that this drug candidate significantly suppressed tumor growth, indicating the therapeutic promise of this class of compounds for the treatment of breast cancer as well as the potential for developing F-radiolabeled imaging ligands as anticancer chemical probes.

Establishing an Analogue Based In Silico Pipeline in the Pursuit of Novel Inhibitory Scaffolds against the SARS Coronavirus 2 Papain-Like Protease

The ongoing coronavirus pandemic has been a burden on the worldwide population, with mass fatalities and devastating socioeconomic consequences. It has particularly drawn attention to the lack of approved small-molecule drugs to inhibit SARS coronaviruses. Importantly, lessons learned from the SARS outbreak of 2002-2004, caused by severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), can be applied to current drug discovery ventures. SARS-CoV-1 and SARS-CoV-2 both possess two cysteine proteases, the main protease (Mpro) and the papain-like protease (PLpro), which play a significant role in facilitating viral replication, and are important drug targets. The non-covalent inhibitor, GRL-0617, which was found to inhibit replication of SARS-CoV-1, and more recently SARS-CoV-2, is the only PLpro inhibitor co-crystallised with the recently solved SARS-CoV-2 PLpro crystal structure. Therefore, the GRL-0617 structural template and pharmacophore features are instrumental in the design and development of more potent PLpro inhibitors. In this work, we conducted scaffold hopping using GRL-0617 as a reference to screen over 339,000 ligands in the chemical space using the ChemDiv, MayBridge, and Enamine screening libraries. Twenty-four distinct scaffolds with structural and electrostatic similarity to GRL-0617 were obtained. These proceeded to molecular docking against PLpro using the AutoDock tools. Of two compounds that showed the most favourable predicted binding affinities to the target site, as well as comparable protein-ligand interactions to GRL-0617, one was chosen for further analogue-based work. Twenty-seven analogues of this compound were further docked against the PLpro, which resulted in two additional hits with promising docking profiles. Our in silico pipeline consisted of an integrative four-step approach: (1) ligand-based virtual screening (scaffold-hopping), (2) molecular docking, (3) an analogue search, and, (4) evaluation of scaffold drug-likeness, to identify promising scaffolds and eliminate those with undesirable properties. Overall, we present four novel, and lipophilic, scaffolds obtained from an exhaustive search of diverse and uncharted regions of chemical space, which may be further explored in vitro through structure-activity relationship (SAR) studies in the search for more potent inhibitors. Furthermore, these scaffolds were predicted to have fewer off-target interactions than GRL-0617. Lastly, to our knowledge, this work contains the largest ligand-based virtual screen performed against GRL-0617.

Translational Scoring of Candidate Treatments for Alzheimer's Disease: A Systematic Approach

BACKGROUND

There are many failures in treatment development for Alzheimer's disease (AD). Some of these failures are the result of development programs that lacked critical information about candidate drugs as these were advanced from one phase of development to the next. Translational scoring (TS) has been proposed as a means of increasing the rigor with which treatment development programs are executed. Previously, these approaches were not specific to AD or to the phase of drug development. Detailed information on the characteristics needed to advance a candidate agent from one phase to the next is the basis for success in subsequent phases.

SUMMARY

The TS approach is presented with a score range of 0-25 for agents entering phases 1, 2, and 3 of development and those that have completed phase 3 and are being considered for regulatory review. Each phase has 5 essential categories scored from 0-5 indicating the completeness of the data available when the agent is being considered for promotion to the next phase. Lower scores suggest that the development program should be reexamined for missing information while higher scores increase the confidence that the agent has the potential to succeed in the next phase. Scoring guidelines are provided and examples of scores for drugs in recent development programs are provided to illustrate the principles of TS. Key Messages: Successful development of drugs for AD treatment requires disciplined informed decision-making at each phase of development. TS is a methodology for more rigorous drug development to help ensure that inadequately characterized drugs are not advanced and that the development platform at each phase is optimal to support success at the next phase.

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

BACKGROUND

Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models.

RESULTS

As part of the Critical Assessment of Massive Data Analysis (CAMDA) "CMap Drug Safety Challenge" 2019 ( http://camda2019.bioinf.jku.at ), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose.

CONCLUSION

Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.

Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene

Additive manufacturing commonly known as 3D printing has numerous applications in several domains including material and biomedical technologies and has emerged as a tool of capabilities by providing fast, highly customized, and cost-effective solutions. However, the impact of the printing materials and chemicals present in the printing fumes has raised concerns about their adverse potential affecting humans and the environment. Thus, it is necessary to understand the properties of the chemicals emitted during additive manufacturing for developing safe and biocompatible fibers having controlled emission of fumes including its sustainable usage. Therefore, in this study, we have developed a computational predictive risk-assessment framework on the comprehensive list of chemicals released during 3D printing using the acrylonitrile butadiene styrene (ABS) filament. Our results showed that the chemicals present in the fumes of the ABS-based fiber used in additive manufacturing have the potential to lead to various toxicity end points such as inhalation toxicity, oral toxicity, carcinogenicity, hepatotoxicity, and teratogenicity. Moreover, because of their absorption, distribution in the body, metabolism, and excretion properties, most of the chemicals exhibited a high absorption level in the intestine and the potential to cross the blood-brain barrier. Furthermore, pathway analysis revealed that signaling like alpha-adrenergic receptor signaling, heterotrimeric G-protein signaling, and Alzheimer's disease-amyloid secretase pathway are significantly overrepresented given the identified target proteins of these chemicals. These findings signify the adversities associated with 3D printing fumes and the necessity for the development of biodegradable and considerably safer fibers for 3D printing technology.

A Robust, Mechanistically Based In Silico Structural Profiler for Hepatic Cholestasis

Owing to the primary role which it holds within metabolism of xenobiotics, the liver stands at heightened risk of exposure to, and injury from, potentially hazardous substances. A principal manifestation of liver dysfunction is cholestasis-the impairment of physiological bile circulation from its point of origin within the organ to the site of action in the small intestine. The capacity for early identification of compounds liable to exert cholestatic effects is of particular utility within the field of pharmaceutical development, where contribution toward candidate attrition is great. Shortcomings associated with the present in vitro methodologies forecasting cholestasis render their predictivity questionable, permitting scope for the adoption of computational toxicology techniques. As such, the intention of this study has been to construct an in silico profiler, founded upon clinical data, highlighting structural motifs most reliably associated with the end point. Drawing upon a list of >1500 small molecular drugs, compiled and annotated by Kotsampasakou, E. and Ecker, G. F. (J. Chem. Inf. Model. 2017, 57, 608-615), we have formulated a series of 15 structural alerts. These describe fragments intrinsic within distinct pharmaceutical classes including psychoactive tricyclics, β-lactam antimicrobials, and estrogenic/androgenic steroids. Description of the coverage and selectivity of each are provided, alongside consideration of the underlying reactive mechanisms and relevant structure-activity concerns. Provision of mechanistic anchoring ensures that potential exists for framing within the adverse outcome pathway paradigm-the chemistry conveyed through the alert, in particular enabling rationalization at the level of the molecular initiating event.

Traditional medicinal plants against replication, maturation and transmission targets of SARS-CoV-2: computational investigation

COVID-19 is an infectious pandemic caused by the SARS-CoV-2 virus. The critical components of SARS-CoV-2 are the spike protein (S-protein) and the main protease (M^pro). M^pro is required for the maturation of the various polyproteins involved in replication and transcription. S-protein helps the SARS-CoV-2 to enter the host cells through the angiotensin-converting enzyme 2 (ACE2). Since ACE2 is required for the binding of SARS-CoV-2 on the host cells, ACE2 inhibitors and blockers have got wider attention, in addition to S-protein and M^pro modulators as potential therapeutics for COVID-19. So far, no specific drugs have shown promising therapeutic potential against COVID-19. The current study was undertaken to evaluate the therapeutic potential of traditional medicinal plants against COVID-19. The bioactives from the medicinal plants, along with standard drugs, were screened for their binding against S-protein, M^pro and ACE2 targets using molecular docking followed by molecular dynamics. Based on the higher binding affinity compared with standard drugs, bioactives were selected and further analyzed for their pharmacological properties such as drug-likeness, ADME/T-test, biological activities using in silico tools. The binding energies of several bioactives analyzed with target proteins were relatively comparable and even better than the standard drugs. Based on Lipinski factors and lower binding energies, seven bioactives were further analyzed for their pharmacological and biological characteristics. The selected bioactives were found to have lower toxicity with a higher GI absorption rate and potent anti-inflammatory and anti-viral activities against targets of COVID-19. Therefore, the bioactives from these medicinal plants can be further developed as phytopharmaceuticals for the effective treatment of COVID-19.

Influence of the UV-Vis irradiation on the acute toxicity to zebrafish and mutagenicity of the selected psychotropic drugs

The influence of the UV-Vis radiation on the toxicity of agomelatine, loxapine, clozapine and tiapride was studied. The phototransformation procedure was conducted with the use of simulated solar radiation. In the case of each compound irradiation time necessary to decompose half of the initial concentration was chosen. The embryotoxicity and acute toxicity were evaluated using zebrafish (Danio rerio) embryos and larvae. The mutagenicity assay was done with the use of a micro-plate Ames test. Generally, the embryotoxicity decreased after the irradiation procedure. The obtained results showed that tiapride is the least toxic compound to zebrafish, however, its toxicity toward larvae increases after the irradiation. Similarly, the mutagenic potential of the mixture of tiapride photoproducts is higher than in the case of parent compound. The phototransformation of loxapine resulted in the change of the acute toxicity profile and increased the rate of reverse mutations in the Ames test. Oppositely, the irradiation of agomelatine caused the decrease of mutagenic potential and acute toxicity was also lower in the postirradiated mixture. The phototransformation of clozapine did not alter the mutagenicity and decreased the acute toxicity to the zebrafish larvae. In silico calculations showed a satisfactory prediction ability in some instances, especially in the case of mutagenic potential of the tiapride phototransformation products.

Mechanisms of Herb-Drug Interactions Involving Cinnamon and CYP2A6: Focus on Time-Dependent Inhibition by Cinnamaldehyde and 2-Methoxycinnamaldehyde

Information is scarce regarding pharmacokinetic-based herb-drug interactions (HDI) with trans-cinnamaldehyde (CA) and 2-methoxycinnamaldehyde (MCA), components of cinnamon. Given the presence of cinnamon in food and herbal treatments for various diseases, HDIs involving the CYP2A6 substrates nicotine and letrozole with MCA (K_S = 1.58 µM; Hill slope = 1.16) and CA were investigated. The time-dependent inhibition (TDI) by MCA and CA of CYP2A6-mediated nicotine metabolism is a complex process involving multiple mechanisms. Molecular dynamic simulations showed that CYP2A6's active site accommodates two dynamic ligands. The preferred binding orientations for MCA and CA were consistent with the observed metabolism: epoxidation, O-demethylation, and aromatic hydroxylation of MCA and cinnamic acid formation from CA. The percent remaining activity plots for TDI by MCA and CA were curved, and they were analyzed with a numerical method using models of varying complexity. The best-fit models support multiple inactivator binding, inhibitor depletion, and partial inactivation. Deconvoluted mass spectra indicated that MCA and CA modified CYP2A6 apoprotein with mass additions of 156.79 (142.54-171.04) and 132.67 (123.37-141.98), respectively, and it was unaffected by glutathione. Heme degradation was observed in the presence of MCA (48.5% ± 13.4% loss; detected by liquid chromatography-tandem mass spectrometry). In the absence of clinical data, HDI predictions were made for nicotine and letrozole using inhibition parameters from the best-fit TDI models and parameters scaled from rats. Predicted area under the concentration-time curve fold changes were 4.29 (CA-nicotine), 4.92 (CA-letrozole), 4.35 (MCA-nicotine), and 5.00 (MCA-letrozole). These findings suggest that extensive exposure to cinnamon (corresponding to ≈ 275 mg CA) would lead to noteworthy interactions. SIGNIFICANCE STATEMENT: Human exposure to cinnamon is common because of its presence in food and cinnamon-based herbal treatments. Little is known about the risk for cinnamaldehyde and methoxycinnamaldehyde, two components of cinnamon, to interact with drugs that are eliminated by CYP2A6-mediated metabolism. The interactions with CYP2A6 are complex, involving multiple-ligand binding, time-dependent inhibition of nicotine metabolism, heme degradation, and apoprotein modification. An herb-drug interaction prediction suggests that extensive exposure to cinnamon would lead to noteworthy interactions with nicotine.

Discovery of nonpeptide 3,4-dihydroquinazoline-4-carboxamides as potent and selective sst2 agonists

Nonpeptide sst2 agonists can provide a new treatment option for patients with acromegaly, carcinoid tumors, and neuroendocrine tumors. Our medicinal chemistry efforts have led to the discovery of novel 3,4-dihydroquinazoline-4-carboxamides as sst2 agonists. This class of molecules exhibits excellent human sst2 potency and selectivity against sst1, sst3, sst4 and sst5 receptors. Leading compound 3-(3-chloro-5-methylphenyl)-6-(3-fluoro-2-hydroxyphenyl)-N,7-dimethyl-N-{[(2S)-pyrrolidin-2-yl]methyl}-3,4-dihydroquinazoline-4-carboxamide (28) showed no inhibition of major CYP450 enzymes (2C9, 2C19, 2D6 and 3A4) and weak inhibition of the hERG channel.

On the Promise of Photopharmacology Using Photoswitches: A Medicinal Chemist's Perspective

Photopharmacology is a growing area of endeavor that employs photoswitchable ligands to allow for light-dependent pharmacological activity. By coupling light to therapeutic action, improved spatial and temporal selectivity can be achieved and subsequently harnessed for new concepts in therapy. Tremendous progress has already been made, with photopharmacological agents now reported against a wide array of target classes and light-dependent results demonstrated in a range of live cell and animal models. Several challenges remain, however, especially in order for photopharmacology to truly impact the clinical management of disease. This Perspective aims to summarize these challenges, particularly with attention to the medicinal chemistry that will be unavoidably required for the further translation of these agents/approaches. By clearly defining challenges for drug hunters, it is hoped that further research into the medicinal chemistry of photopharmacological agents will be stimulated, ultimately enabling full realization of the huge potential for this exciting field.

Data science-driven analyses of drugs inducing hypertension as an adverse effect

The utilization of approved medication is a requisite to combat certain diseases for health; however, the undesirable adverse effects (AEs) due to medication are generally unavoidable. Hypertension is one of such AEs resulting from approved medication in which blood pressure in the arteries gets elevated and is a risk factor for several diseases including heart and kidney failure. HTs are the approved drugs that can cause hypertension as an AE. Here, the goal of the study is to investigate the structural and functional diversities of HTs. In our quest to unravel the structural parameters of the HTs, a systematic analysis of the HTs having a different number and type of ring systems was conducted. The cellular component, molecular function and biological processes adopted by the gene products were analyzed. Moreover, our systematically done analysis suggests that all the target families are active in a common pathway, that is, nerve transmission. A comparison of the selected structural and functional aspect of HTs with anti-hypertensives suggests that HTs follow certain structural and functional features in spite of many possibilities. Our study provides a promising methodology that considers the influence of structural diversity of AE causing agents on a functional perspective for precursory clinical decision making. This could be extended to explore the structural and functional trends that are adopted by agents causing certain diseases or AEs.

Metabolism and Bioactivation: It's Time to Expect the Unexpected

Improvements in in vitro ADME tools and pharmacokinetic prediction models have helped to shift attrition rates in early clinical trials from poor exposure to drug safety concerns, such as drug-induced liver injury (DILI). Assessing a new chemical entity's potential for liver toxicity is an important consideration for the likely success of new drug candidates. Reactive intermediates produced during drug metabolism have been implicated as a cause of DILI, and their formation has been correlated to the addition of a black box warning on a drug label. In this work, we will present contemporary examples of the bioactivation of atypical structures usually regarded as benign and often used by medicinal chemists when attempting to avoid bioactivation. Medicinal chemistry strategies used to derisk bioactivation will be discussed, and an emphasis will be placed on the necessity of a multidisciplinary approach.

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights.

This article is categorized under:

Structure and Mechanism > Computational Biochemistry and Biophysics

Data Science > Chemoinformatics

Detection and characterization of olmutinib reactive metabolites by LC-MS/MS: Elucidation of bioactivation pathways

Olmutinib (Olita™) is an orally bioavailable third generation epidermal growth factor receptor tyrosine kinase inhibitor. Olmutinib was approved in South Korea in May 2016 for the treatment of patients suffering from locally advanced or metastatic epidermal growth factor receptor T790M mutation-positive non-small cell lung cancer. Reactive olmutinib intermediates may be responsible for the severe side effects associated with the treatment. However, literature review revealed no previous reports on the structural identification of reactive olmutinib metabolites. In this work, the formation of reactive olmutinib metabolites in rat liver microsomes was investigated. Methoxylamine, glutathione, and potassium cyanide were used as capturing agents for aldehyde, iminoquinones, and iminium intermediates, respectively. The stable complexes formed were identified using liquid chromatography-tandem mass spectrometry. The major phase I metabolic pathway observed in vitro was hydroxylation of the piperazine ring. Seven potential reactive intermediates were characterized, including three iminium ions, three iminoquinones, and one aldehyde. Based on the findings, various bioactivation pathways were postulated. Hence, identifying the reactive intermediates of olmutinib that may be the cause of severe side effects can provide new insights, leading to improved treatments for patients.

Strategies for Early Prediction and Timely Recognition of Drug-Induced Liver Injury: The Case of Cyclin-Dependent Kinase 4/6 Inhibitors

The idiosyncratic nature of drug-induced liver injury (DILI) represents a current challenge for drug developers, regulators and clinicians. The myriad of agents (including medications, herbals, and dietary supplements) with recognized DILI potential not only strengthens the importance of the post-marketing phase, when urgent withdrawal sometimes occurs for rare unanticipated liver toxicity, but also shows the imperfect predictivity of pre-clinical models and the lack of validated biomarkers beyond traditional, non-specific liver function tests. After briefly reviewing proposed key mechanisms of DILI, we will focus on drug-related risk factors (physiochemical and pharmacokinetic properties) recently proposed as predictors of DILI and use cyclin-dependent kinase 4/6 inhibitors, relatively novel oral anticancer medications approved for breast cancer, as a case study to discuss the feasibility of early detection of DILI signals during drug development: published data from pivotal clinical trials, unpublished post-marketing reports of liver adverse events, and pharmacokinetic properties will be used to provide a comparative evaluation of their liver safety and gain insight into drug-related risk factors likely to explain the observed differences.

Synthesis and exploration of a novel chlorobenzylated 2-aminothiazole-phenyltriazole hybrid as migratory inhibitor of B16F10 in melanoma cells

In the study presented here, a novel chlorobenzylated bi-heterocyclic hybrid molecule (7) was synthesized and its structural confirmation was carried out by IR, ¹H-NMR, ¹³C-NMR and CHN analysis data. This compound 7 was subjected to biological study with B16F10 mouse melanoma cells. The anti-proliferative results showed that 7 showed no significant toxicity at concentrations ranging of 0–44 μM. The treatment of B16F10 cells with 7 at aforementioned concentration range indicated that migration of cells was significantly lower than that of the control cells in a dose dependent manner. The possible migration inhibitory effect of these melanoma cells was further evaluated through gelatinolytic activity of MMP-2 and MMP-9 secreted from B16F10 cells. It was inferred from our results that 7 was not affecting the expression and activity of these enzymes. Some other zinc-dependent matrix metalloproteinases (MMPs) were involved in the inhibitory progression. Taken together, compound 7 inhibited migrations of B16F10 mouse melanoma cells. Therefore, it may deserve consideration as a potential agent for the treatment of cancer.

Pharmacological safety of Plinia cauliflora (Mart.) Kausel in rabbits

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Fruit peels of Plinia cauliflora are widely used in Brazilian traditional medicine.

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No studies have proved the safety of its pharmacological effects.

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We assessed the safety pharmacology of P. cauliflora extract (EEPC) in rabbits.

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EEPC did not cause any significant changes in several physiological systems.

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These data provide important safety data for its clinical use.

Fruit peels of Plinia cauliflora (Mart.) Kausel are widely used in Brazilian traditional medicine, but no studies have proved the safety of its pharmacological effects on the respiratory, cardiovascular, and central nervous systems. The present study assessed the safety pharmacology of P. cauliflora in New Zealand rabbits. First, an ethanol extract (EEPC) was selected for the pharmacological experiments and chemical characterization. Then, different groups of rabbits were orally treated with EEPC (200 and 2000 mg/kg) or vehicle. Acute behavioral and physiological alterations in the modified Irwin test, respiratory rate, arterial blood gas, and various cardiovascular parameters (i.e., heart rate, blood pressure, and electrocardiography) were evaluated. The main secondary metabolites that were identified in EEPC were ellagic acid, gallic acid, O-deoxyhexosyl quercetin, and the anthocyanin O-hexosyl cyanidin. No significant behavioral or physiological changes were observed in any of the groups. None of the doses of EEPC affected respiratory rate or arterial blood gas, with no changes on blood pressure or electrocardiographic parameters. The present study showed that EEPC did not cause any significant changes in respiratory, cardiovascular, or central nervous system function. These data provide scientific evidence of the effects of this species and important safety data for its clinical use.