Molecular Modeling Strategies of Cancer Multidrug Resistance

Targeting O-GlcNAcylation in cancer therapeutic resistance: The sugar Saga continues

O-linked-N-acetylglucosaminylation (O-GlcNAcylation), a dynamic post-translational modification (PTM), holds profound implications in controlling various cellular processes such as cell signaling, metabolism, and epigenetic regulation that influence cancer progression and therapeutic resistance. From the therapeutic perspective, O-GlcNAc modulates drug efflux, targeting and metabolism. By integrating signals from glucose, lipid, amino acid, and nucleotide metabolic pathways, O-GlcNAc acts as a nutrient sensor and transmits signals to exerts its function on genome stability, epithelial-mesenchymal transition (EMT), cell stemness, cell apoptosis, autophagy, cell cycle. O-GlcNAc also attends to tumor microenvironment (TME) and the immune response. At present, several strategies aiming at targeting O-GlcNAcylation are under mostly preclinical evaluation, where the newly developed O-GlcNAcylation inhibitors markedly enhance therapeutic efficacy. Here we systematically outline the mechanisms through which O-GlcNAcylation influences therapy resistance and deliberate on the prospects and challenges associated with targeting O-GlcNAcylation in future cancer treatments.

Effects of docetaxel on metastatic prostate (DU-145) carcinoma cells cultured as 2D monolayers and 3D multicellular tumor spheroids

Docetaxel (DTX) is one of the chemotherapeutic drugs indicated as a first-line treatment against metastatic prostate cancer (mPCa). This study aimed to compare the impact of DTX on mPCa (DU-145) tumor cells cultured as 2D monolayers and 3D multicellular tumor spheroids (MCTS) in vitro. The cells were treated with DTX (1-96 µM) at 24, 48, or 72 hr in cell viability assays (resazurin, phosphatase acid, and lactate dehydrogenase). Cell death was assessed with fluorescent markers and proliferation by clonogenic assay (2D) and morphology, volume, and integrity assay (3D). The cell invasion was determined using transwell (2D) and extracellular matrix (ECM) (3D). Results showed that DTX decreased cell viability in both culture models. In 2D, the IC50 (72 hr) values were 11.06 μM and 14.23 μM for resazurin and phosphatase assays, respectively. In MCTS, the IC50 values for the same assays were 114.9 μM and 163.7 μM, approximately 10-fold higher than in the 2D model. The % of viable cells decreased, while the apoptotic cell number was elevated compared to the control in 2D. In 3D spheroids, only DTX 24 μM induced apoptosis. DTX (≥24 μM at 216 hr) lowered the volume, and DTX 96 μM completely disintegrated the MCTS. DTX reduced the invasion of mPCa cells to matrigel (2D) and migration from MCTS to the ECM. Data demonstrated significant differences in drug response between 2D and 3D cell culture models using mPCa DU-145 tumor cells. MCTS resembles the early stages of solid tumors in vivo and needs to be considered in conjunction with 2D cultures when searching for new therapeutic targets.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

The role and application of vesicles in triple-negative breast cancer: Opportunities and challenges

Extracellular vesicles (EVs) carry DNA, RNA, protein, and other substances involved in intercellular crosstalk and can be used for the targeted delivery of drugs. Triple-negative breast cancer (TNBC) is rich in recurrent and metastatic disease and lacks therapeutic targets. Studies have proved the role of EVs in the different stages of the genesis and development of TNBC. Cancer cells actively secrete various biomolecules, which play a significant part establishing the tumor microenvironment via EVs. In this article, we describe the roles of EVs in the tumor immune microenvironment, metabolic microenvironment, and vascular remodeling, and summarize the application of EVs for objective delivery of chemotherapeutic drugs, immune antigens, and cancer vaccine adjuvants. EVs-based therapy may represent the next-generation tool for targeted drug delivery for the cure of a variety of diseases lacking effective drug treatment.

GSK2606414 Sensitizes ABCG2-Overexpressing Multidrug-Resistant Colorectal Cancer Cells to Chemotherapeutic Drugs

Colorectal cancer is a common malignant tumor. A major factor in the high mortality rate of colorectal cancer is the emergence of multidrug resistance (MDR). Overexpression of the ABCG2 gene in cancer cells directly leads to MDR. Finding new inhibitors of ABCG2 may be an effective way to overcome drug resistance. We found that the compound GSK2606414 enhanced the sensitivity of the ABCG2 substrate to the chemotherapeutic drugs mitoxantrone and doxorubicin in ABCG2-overexpressing multidrug-resistant colorectal cancer cells by increasing their intracellular accumulation without affecting the protein expression of ABCG2. Molecular docking experiments predicted that GSK2606414 could stably bind in the drug-binding pocket of ABCG2. In conclusion, GSK2606414 can sensitize ABCG2-overexpressed multidrug-resistant colorectal cancer cells to chemotherapy drugs and can be used as a potential inhibitor of ABCG2.

Advances in the structure, mechanism and targeting of chemoresistance-linked ABC transporters

Cancer cells frequently display intrinsic or acquired resistance to chemically diverse anticancer drugs, limiting therapeutic success. Among the main mechanisms of this multidrug resistance is the overexpression of ATP-binding cassette (ABC) transporters that mediate drug efflux, and, specifically, ABCB1, ABCG2 and ABCC1 are known to cause cancer chemoresistance. High-resolution structures, biophysical and in silico studies have led to tremendous progress in understanding the mechanism of drug transport by these ABC transporters, and several promising therapies, including irradiation-based immune and thermal therapies, and nanomedicine have been used to overcome ABC transporter-mediated cancer chemoresistance. In this Review, we highlight the progress achieved in the past 5 years on the three transporters, ABCB1, ABCG2 and ABCC1, that are known to be of clinical importance. We address the molecular basis of their broad substrate specificity gleaned from structural information and discuss novel approaches to block the function of ABC transporters. Furthermore, genetic modification of ABC transporters by CRISPR-Cas9 and approaches to re-engineer amino acid sequences to change the direction of transport from efflux to import are briefly discussed. We suggest that current information regarding the structure, mechanism and regulation of ABC transporters should be used in clinical trials to improve the efficiency of chemotherapeutics for patients with cancer.

Using CADD tools to inhibit the overexpressed genes FAP, FN1, and MMP1 by repurposing ginsenoside C and Rg1 as a treatment for oral cancer

Oral cancer is one of the most common cancer types. Many factors can express certain genes that cause the proliferation of oral tissues. Overexpressed genes were detected in oral cancer patients; three were highly impacted. FAP, FN1, and MMP1 were the targeted genes that showed inhibition results in silico by ginsenoside C and Rg1. Approved drugs were retrieved from the DrugBank database. The docking scores show an excellent interaction between the ligands and the targeted macromolecules. Further molecular dynamics simulations showed the binding stability of the proposed natural products. This work recommends repurposing ginsenoside C and Rg1 as potential binders for the selected targets and endorses future experimental validation for the treatment of oral cancer.

Novel computational and drug design strategies for inhibition of human papillomavirus-associated cervical cancer and DNA polymerase theta receptor by Apigenin derivatives

The present study deals with the advanced in-silico analyses of several Apigenin derivatives to explore human papillomavirus-associated cervical cancer and DNA polymerase theta inhibitor properties by molecular docking, molecular dynamics, QSAR, drug-likeness, PCA, a dynamic cross-correlation matrix and quantum calculation properties. The initial literature study revealed the potent antimicrobial and anticancer properties of Apigenin, prompting the selection of its potential derivatives to investigate their abilities as inhibitors of human papillomavirus-associated cervical cancer and DNA polymerase theta. In silico molecular docking was employed to streamline the findings, revealing promising energy-binding interactions between all Apigenin derivatives and the targeted proteins. Notably, Apigenin 4'-O-Rhamnoside and Apigenin-4'-Alpha-L-Rhamnoside demonstrated higher potency against the HPV45 oncoprotein E7 (PDB ID 2EWL), while Apigenin and Apigenin 5-O-Beta-D-Glucopyranoside exhibited significant binding energy against the L1 protein in humans. Similarly, a binding affinity range of - 7.5 kcal/mol to - 8.8 kcal/mol was achieved against DNA polymerase theta, indicating the potential of Apigenin derivatives to inhibit this enzyme (PDB ID 8E23). This finding was further validated through molecular dynamic simulation for 100 ns, analyzing parameters such as RMSD, RMSF, SASA, H-bond, and RoG profiles. The results demonstrated the stability of the selected compounds during the simulation. After passing the stability testing, the compounds underwent screening for ADMET, pharmacokinetics, and drug-likeness properties, fulfilling all the necessary criteria. QSAR, PCA, dynamic cross-correlation matrix, and quantum calculations were conducted, yielding satisfactory outcomes. Since this study utilized in silico computational approaches and obtained outstanding results, further validation is crucial. Therefore, additional wet-lab experiments should be conducted under in vivo and in vitro conditions to confirm the findings.

The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity

The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy, and toxicity as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biologic membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiologic functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms. The data collected contributes toward safer and more effective pharmacological treatments. SIGNIFICANCE STATEMENT: The experimental structure of ATP-binding cassette and solute carrier transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms.

Drug resistance and new therapies in gallbladder cancer

Gallbladder cancer (GBC) is a highly aggressive malignancy, which poses significant challenges for timely diagnosis, resulting in a dismal prognosis. Chemotherapy serves as a primary treatment option in cases where surgery is not feasible. However, the emergence of chemoresistance poses a significant challenge to the effectiveness of chemotherapy, ultimately resulting in a poor prognosis. Despite extensive research on mechanisms of chemotherapeutic resistance in oncology, the underlying mechanisms of chemoresistance in GBC remain poorly understood. In this review, we present the findings from the last decade on the molecular mechanisms of chemotherapeutic resistance in GBC. We hope that these insights may provide novel therapeutic and experimental targets for further investigations into this lethal disease.

Flavonoids as P-glycoprotein inhibitors for multidrug resistance in cancer: an in-silico approach

Cancer has become a leading cause of mortality due to non-communicable diseases after cardiovascular disease worldwide and is increasing day by day at a daunting pace. According to an estimate by 2040 there will be 28.4 million cancer cases. Occurrence of multidrug resistance has further worsened the scenario of available cancer treatment. Among different mechanisms of multidrug resistance efflux of xenobiotics by ABC transporter is of prime importance. P-glycoprotein (P-gp) is the major factor behind occurrence of multidrug resistance due to its wide distribution and invariably big binding cavity. Various generations of chemical inhibitors for P-gp have been designed and tested are not devoid of major side effects. Thus, in present study flavonoids a major class of natural compounds was virtually screened in order to find molecules which can be used as selective P-gp inhibitors to be used along with chemotherapeutics. After screening 4275 molecules from different classes of flavonoids i.e. flavan, flavanol, flavonone, flavone, anthocyanins, and isoflavone, through Glide docking top ten hit molecules were selected based on their binding affinity, binding energy calculation and pharmacokinetic properties. All the hit molecules were found to have docking score within the range of -11.202 to -9.699 kcal/mol showing very strong interaction with the amino acid residues of binding pocket. Whereas, dock score of standard P-gp inhibitor verapamil was -4.984 kcal/mol. The ligand and protein complex were found to be quite stable while run through molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

Current development of pyrazole-azole hybrids with anticancer potential

Chemotherapy is a critical treatment modality for cancer patients, but multidrug resistance remains one of the major challenges in cancer therapy, creating an urgent need for the development of novel potent chemical entities. Azoles, particularly pyrazole, could interact with different biological targets and exhibit diverse biological properties including anticancer activity. Many clinically used anticancer agents own an azole moiety, demonstrating that azoles are privileged and pivotal templates in the discovery of novel anticancer chemotherapeutics. The present article is an attempt to highlight the recent advances in pyrazole-azole hybrids with anticancer potential and discuss the structure-activity relationships, covering articles published from 2018 to present, to facilitate the rational design of more effective anticancer candidates.

Molecular simulations required to target novel and potent inhibitors of cancer invasion

INTRODUCTION

Computer-aided drug design (CADD) is a computational approach used to discover, develop, and analyze drugs and active molecules with similar biochemical properties. Molecular simulation technology has significantly accelerated drug research and reduced manufacturing costs. It is an optimized drug discovery method that greatly improves the efficiency of novel drug development processes.

AREASCOVERED

This review discusses the development of molecular simulations of effective cancer inhibitors and traces the main outcomes of in silico studies by introducing representative categories of six important anticancer targets. The authors provide views on this topic from the perspective of both medicinal chemistry and artificial intelligence, indicating the major challenges and predicting trends.

EXPERT OPINION

The goal of introducing CADD into cancer treatment is to realize a highly efficient, accurate, and desired approach with a high success rate for identifying potent drug candidates. However, the major challenge is the lack of a sophisticated data-filtering mechanism to verify bottom data from mixed-quality references. Consequently, despite the continuous development of algorithms, computer power, and interface optimization, specific data filtering mechanisms will become an urgent and crucial issue in the future.

Disruption of Irisin Dimerization by FDA-Approved Drugs: A Computational Repurposing Approach for the Potential Treatment of Lipodystrophy Syndromes

In this paper, we present the development of a computer-based repurposing approach to identify FDA-approved drugs that are potentially able to interfere with irisin dimerization. It has been established that altered levels of irisin dimers are a pure hallmark of lipodystrophy (LD) syndromes. Accordingly, the identification of compounds capable of slowing down or precluding the irisin dimers' formation could represent a valuable therapeutic strategy in LD. Combining several computational techniques, we identified five FDA-approved drugs with satisfactory computational scores (iohexol, XP score = -7.70 kcal/mol, SP score = -5.5 kcal/mol, ΔG_bind = -61.47 kcal/mol, ΔG_bind (average) = -60.71 kcal/mol; paromomycin, XP score = -7.23 kcal/mol, SP score = -6.18 kcal/mol, ΔG_bind = -50.14 kcal/mol, ΔG_bind (average) = -49.13 kcal/mol; zoledronate, XP score = -6.33 kcal/mol, SP score = -5.53 kcal/mol, ΔG_bind = -32.38 kcal/mol, ΔG_bind (average) = -29.42 kcal/mol; setmelanotide, XP score = -6.10 kcal/mol, SP score = -7.24 kcal/mol, ΔG_bind = -56.87 kcal/mol, ΔG_bind (average) = -62.41 kcal/mol; and theophylline, XP score = -5.17 kcal/mol, SP score = -5.55 kcal/mol, ΔG_bind = -33.25 kcal/mol, ΔG_bind (average) = -35.29 kcal/mol) that are potentially able to disrupt the dimerization of irisin. For this reason, they deserve further investigation to characterize them as irisin disruptors. Remarkably, the identification of drugs targeting this process can offer novel therapeutic opportunities for the treatment of LD. Furthermore, the identified drugs could provide a starting point for a repositioning approach, synthesizing novel analogs with improved efficacy and selectivity against the irisin dimerization process.

The Battlefield of Chemotherapy in Pediatric Cancers

The survival rate for pediatric cancers has remarkably improved in recent years. Conventional chemotherapy plays a crucial role in treating pediatric cancers, especially in low- and middle-income countries where access to advanced treatments may be limited. The Food and Drug Administration (FDA) approved chemotherapy drugs that can be used in children have expanded, but patients still face numerous side effects from the treatment. In addition, multidrug resistance (MDR) continues to pose a major challenge in improving the survival rates for a significant number of patients. This review focuses on the severe side effects of pediatric chemotherapy, including doxorubicin-induced cardiotoxicity (DIC) and vincristine-induced peripheral neuropathy (VIPN). We also delve into the mechanisms of MDR in chemotherapy to the improve survival and reduce the toxicity of treatment. Additionally, the review focuses on various drug transporters found in common types of pediatric tumors, which could offer different therapeutic options.

Multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis of the synergistic effects between natural compounds baicalein and cubebin for the inhibition of the main protease of SARS-CoV-2

Combination drugs have been used for several diseases for many years since they produce better therapeutic effects. However, it is still a challenge to discover candidates to form a combination drug. This study aimed to investigate whether using a comprehensive in silico approach to identify novel combination drugs from a Chinese herbal formula is an appropriate and creative strategy. We, therefore, used Toujie Quwen Granules for the main protease (M^pro) of SARS-CoV-2 as an example. We first used molecular docking to identify molecular components of the formula which may inhibit M^pro. Baicalein (HQA004) is the most favorable inhibitory ligand. We also identified a ligand from the other component, cubebin (CHA008), which may act to support the proposed HQA004 inhibitor. Molecular dynamics simulations were then performed to further elucidate the possible mechanism of inhibition by HQA004 and synergistic bioactivity conferred by CHA008. HQA004 bound strongly at the active site and that CHA008 enhanced the contacts between HQA004 and M^pro. However, CHA008 also dynamically interacted at multiple sites, and continued to enhance the stability of HQA004 despite diffusion to a distant site. We proposed that HQA004 acted as a possible inhibitor, and CHA008 served to enhance its effects via allosteric effects at two sites. Additionally, our novel wavelet analysis showed that as a result of CHA008 binding, the dynamics and structure of M^pro were observed to have more subtle changes, demonstrating that the inter-residue contacts within M^pro were disrupted by the synergistic ligand. This work highlighted the molecular mechanism of synergistic effects between different herbs as a result of allosteric crosstalk between two ligands at a protein target, as well as revealed that using the multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis to discover novel combination drugs from a Chinese herbal remedy is an innovative pathway.

The Molecular Mechanisms of Oleanane Aldehyde-β-enone Cytotoxicity against Doxorubicin-Resistant Cancer Cells

Oleanane aldehyde-β-enone (OA), being the semi-synthetic derivative of the triterpenoid betulin, effectively inhibits the proliferation of HBL-100 and K562 cancer cells (IC50 0.47–0.53 µM), as well as the proliferation of their resistant subclones with high P-gp expression HBL-100/Dox, K562/i-S9 and K562/i-S9_Dox (IC50 0.45−1.24 µM). A molecular docking study, rhodamine efflux test, synergistic test with Dox, and ABC transporter gene expression were used to investigate the ability of OA to act as a P-gp substrate or inhibitor against Dox-resistant cells. We noted a trend toward a decrease in ABCB1, ABCC1 and ABCG2 expression in HBL-100 cells treated with OA. The in silico and in vitro methods suggested that OA is neither a direct inhibitor nor a competitive substrate of P-gp in overexpressing P-gp cancer cells. Thus, OA is able to overcome cellular resistance and can accumulate in Dox-resistant cells to realize toxic effects. The set of experiments suggested that OA toxic action can be attributed to activating intrinsic/extrinsic or only intrinsic apoptosis pathways in Dox-sensitive and Dox-resistant cancer cells, respectively. The cytotoxicity of OA in resistant cells is likely mediated by a mitochondrial cell death pathway, as demonstrated by positive staining with Annexin V–FITC, an increasing number of cells in the subG0/G1 phase, reactive oxygen species generation, mitochondrial dysfunction, cytochrome c migration and caspases-9,-6 activation.

Hydroxychavicol as a potential anticancer agent (Review)

Piper betle leaves are widely cultivated in Malaysia, India, Indonesia and Thailand. They have been used as a traditional medicine for centuries due to their medicinal properties, including antioxidant, antiproliferative, antibacterial, antifungal and anti-inflammatory properties, which are attributable to their high phenolic contents. Hydroxychavicol (HC), a primary constituent of P. betle leaves, is known to possess antiproliferative activity at micromolar doses on various cancer cell lines of different origins while leaving normal cells unharmed. The present review summarises the mechanisms of action of HC reported in the literature, reviews the scope of work done thus far and outlines the direction of future research on the potential of HC as an anticancer agent. PubMed, Scopus and Web of Science were searched using the keywords (hydroxychavicol OR 4-allylpyrocatechol OR 4-allylcatechol) AND (cancer OR carcinogenesis OR tumour OR carcinoma) to acquire research articles. In vitro studies reported several possible mechanisms for the chemopreventive effects of HC against cancer cell lines, including chronic myelogenous leukaemia (CML), prostate, glioma, breast and colorectal cancers, while in vivo studies encompassed investigations on Ehrlich ascites carcinoma cells in Swiss albino mice and a CML mouse model. These studies suggest that HC exerts its anticancer effect via the modulation of mitochondrial membrane potential and the c-Jun N-terminal kinase, mitogen-activated protein kinase and endoplasmic reticulum-unfolded protein responses pathways and the generation of reactive oxygen species. In summary, future research should focus on combinations of HC with other anticancer drugs and testing in animal models to evaluate its bioavailability, potency and tissue and dose selectivity.

A new era for the design of TRPV1 antagonists and agonists with the use of structural information and molecular docking of capsaicin-like compounds

The design of TRPV1 antagonists and agonists has reached a new era since TRPV1 structures at near-atomic resolution are available. Today, the ligand-binding forms of several classical antagonists and agonists are known; therefore, the specific role of key TRPV1’s residues in binding of ligands can be elucidated. It is possible to place the well-defined pharmacophore of TRPV1 ligands, conformed by head, neck, and tail groups, in the right pocket regions of TRPV1. It will allow a more thorough use of molecular modelling methods to conduct more effective rational drug design protocols. In this work, important points about the interactions between TRPV1 and capsaicin-like compounds are spelled out, based on the known pharmacophore of the ligands and the already available TRPV1 structures. These points must be addressed to generate reliable poses of novel candidates and should be considered during the design of novel TRPV1 antagonists and agonists.

Special Issue: "New Diagnostic and Therapeutic Tools against Multidrug-Resistant Tumors (STRATAGEM Special Issue, EU-COST CA17104)"

Cancer drug resistance, either intrinsic or acquired, often causes treatment failure and increased mortality [...].

DrugMAP: molecular atlas and pharma-information of all drugs

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

Small molecule inhibitors targeting the cancers

Compared with traditional therapies, targeted therapy has merits in selectivity, efficacy, and tolerability. Small molecule inhibitors are one of the primary targeted therapies for cancer. Due to their advantages in a wide range of targets, convenient medication, and the ability to penetrate into the central nervous system, many efforts have been devoted to developing more small molecule inhibitors. To date, 88 small molecule inhibitors have been approved by the United States Food and Drug Administration to treat cancers. Despite remarkable progress, small molecule inhibitors in cancer treatment still face many obstacles, such as low response rate, short duration of response, toxicity, biomarkers, and resistance. To better promote the development of small molecule inhibitors targeting cancers, we comprehensively reviewed small molecule inhibitors involved in all the approved agents and pivotal drug candidates in clinical trials arranged by the signaling pathways and the classification of small molecule inhibitors. We discussed lessons learned from the development of these agents, the proper strategies to overcome resistance arising from different mechanisms, and combination therapies concerned with small molecule inhibitors. Through our review, we hoped to provide insights and perspectives for the research and development of small molecule inhibitors in cancer treatment.

Trends in FDA Transporter-Based Post Marketing Requirements and Commitments Over the Last Decade

Characterizing interactions between new molecular entities (NMEs) and drug transporters is a critical element of drug development that helps in assessing potential transporter-based drug-drug interactions (DDIs). However, not all NME new drug applications (NDAs) include a full characterization of NMEs transporter-based DDI, which necessitates the issuance of post marketing requirement (PMR)/post marketing commitment (PMC) by the US Food and Drug Administration (FDA) to characterize these potential interactions. The objective of this analysis is to identify trends in transporter-based PMRs/PMCs issued by the FDA between 2012 and 2021. A decrease in the number of transporter-based PMRs/PMCs was observed from 2012 to 2016 and an increasing trend in the number of PMRs/PMCs was observed after 2017. The majority of these transporter-based PMRs/PMCs requested clinical evaluation (48%), some requested in vitro assessment (38%), and 2.5% requested modeling and simulation assessment. Most of the PMRs/PMCs requested evaluation of NMEs as perpetrator with the efflux transporters, P-gp and/or BCRP (53%). Forty-eight percent of the PMRs/PMCs were fulfilled with 67% resulted in labelling updates. On average 2.5 years were needed for the information related to PMRs/PMCs to show in NMEs labeling. In conclusion, this analysis highlights the increased emphasis from the FDA on proper characterization of transporter-based DDI and call for the need of early characterization of NMEs-transporters interaction before initial NDA approval.

A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance

Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system’s time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system.