Fragment-based drug design facilitates selective kinase inhibitor discovery

New drug approvals for 2022: Synthesis and clinical applications

The U.S. Food and Drug Administration has approved a total of 37 new drugs in 2022, which are composed of 20 chemical entities and 17 biologics. In particular, 20 chemical entities, including 17 small molecule drugs, 1 radiotherapy, and 2 diagnostic agents, provide privileged scaffolds, breakthrough clinical benefits, and a new mechanism of action for the discovery of more potent clinical candidates. The structure-based drug development with clear targets and fragment-based drug development with privileged scaffolds have always been the important modules in the field of drug discovery, which could easily bypass the patent protection and bring about improved biological activity. Therefore, we summarized the relevant valuable information about clinical application, mechanism of action, and chemical synthesis of 17 newly approved small molecule drugs in 2022. We hope this timely and comprehensive review could bring about creative and elegant inspiration on the synthetic methodologies and mechanism of action for the discovery of new drugs with novel chemical scaffolds and extended clinical indications.

Comprehensive analysis of crystal structure, spectroscopic properties, quantum chemical insights, and molecular docking studies of two pyrazolopyridine compounds: potential anticancer agents

In this study, two pyrazolo[3,4-b]pyridine derivatives (4a and 4b) were grown using a slow evaporation solution growth technique and characterized by FT-IR, HRMS, 1H/13C NMR spectroscopy, and X-ray crystallography. The 4a and 4b structures crystallized in monoclinic and triclinic systems with space groups P21/n and P1̄, respectively. Theoretical calculations were performed at the DFT/B3LYP level for the optimized geometries. The results were in excellent agreement with the experimental data (spectroscopic and XRD). This investigation encompasses molecular modeling studies including Hirshfeld surface analysis, energy framework calculations, and frontier molecular orbital analysis. Intermolecular interactions within the crystal structures of the compounds were explored through Hirshfeld surface analysis, which revealed the notable presence of hydrogen bonding and hydrophobic interactions. This insight provides valuable information on the structural stability and potential solubility characteristics of these compounds. The research was extended to docking analysis with eight distinct kinases (BRAF, HER2, CSF1R, MEK2, PDGFRA, JAK, AKT1, and AKT2). The results of this analysis demonstrate that both 4a and 4b interact effectively with the kinase-binding sites through a combination of hydrophobic interactions and hydrogen bonding. Compound 4a had the best affinity for proteins; this is related to the fact that the compound is not rigid and has a small size, allowing it to sit well at any binding site. This study contributes to the advancement of kinase inhibitor research and offers potential avenues for the development of new therapeutic agents for cancer treatment.

TWN-FS method: A novel fragment screening method for drug discovery

Fragment-based drug discovery (FBDD) is a well-established and effective method for generating diverse and novel hits in drug design. Kinases are suitable targets for FBDD due to their well-defined structure. Water molecules contribute to structure and function of proteins and also influence the environment within the binding pocket. Water molecules form a variety of hydrogen-bonded cyclic water-ring networks, collectively known as topological water networks (TWNs). Analyzing the TWNs in protein binding sites can provide valuable insights into potential locations and shapes for fragments within the binding site. Here, we introduce TWN-based fragment screening (TWN-FS) method, a novel screening method that suggests fragments through grouped TWN analysis within the protein binding site. We used this method to screen known CDK2, CHK1, IGF1R and ERBB4 inhibitors. Our findings suggest that TWN-FS method has the potential to effectively screen fragments. The TWN-FS method package is available on GitHub at https://github.com/pkj0421/TWN-FS.

FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization

Recently, deep generative models have been regarded as promising tools in fragment-based drug design (FBDD). Despite the growing interest in these models, they still face challenges in generating molecules with desired properties in low data regimes. In this study, we propose a novel flow-based autoregressive model named FFLOM for linker and R-group design. In a large-scale benchmark evaluation on ZINC, CASF, and PDBbind test sets, FFLOM achieves state-of-the-art performance in terms of validity, uniqueness, novelty, and recovery of the generated molecules and can recover over 92% of the original molecules in the PDBbind test set (with at least five atoms). FFLOM also exhibits excellent potential applicability in several practical scenarios encompassing fragment linking, PROTAC design, R-group growing, and R-group optimization. In all four cases, FFLOM can perfectly reconstruct the ground-truth compounds and generate over 74% of molecules with novel fragments, some of which have higher binding affinity than the ground truth.

Discovery of novel 6-p-tolyl-3-(3,4,5-trimethoxybenzyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine derivative as a potent tubulin inhibitor with promising in vivo antitumor activity

Building on our prior research, a novel series of trimethoxyphenoxymethyl- and trimethoxybenzyl-substituted triazolothiadiazine compounds has been designed and achieved successfully via a direct ring-closing strategy. Initial biological evaluation illustrated that the most active derivative B₅ exhibited significant cell growth inhibitory activity toward HeLa, HT-29, and A549 giving the IC₅₀ values of 0.046, 0.57, and 0.96 μM, respectively, which are greater or similar with CA-4. The mechanism study revealed that B₅ caused the G₂/M phase arrest, induced cell apoptosis in HeLa cells in a concentration-dependent manner, and also showed potent tubulin polymerization inhibitory effect. Meanwhile, B₅ exerted significant antivascular activity in the wound-healing and tube formation assays. Most importantly, B₅ remarkably inhibited tumor growth without obvious signs of toxicity in A549-xenograft mice model. These observations indicate that 6-p-tolyl-3-(3,4,5-trimethoxybenzyl)-7H-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazine might be considered as the potential lead compound to develop highly efficient anticancer agents with potent selectivity over normal human cells.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Emerging approaches to CDK inhibitor development, a structural perspective

Aberrant activity of the cyclin-dependent kinase family is frequently noted in a number of diseases identifying them as potential targets for drug development. However, current CDK inhibitors lack specificity owing to the high sequence and structural conservation of the ATP binding cleft across family members, highlighting the necessity of finding novel modes of CDK inhibition. The wealth of structural information regarding CDK assemblies and inhibitor complexes derived from X-ray crystallographic studies has been recently complemented through the use of cryo-electron microscopy. These recent advances have provided insights into the functional roles and regulatory mechanisms of CDKs and their interaction partners. This review explores the conformational malleability of the CDK subunit, the importance of SLiM recognition sites in CDK complexes, the progress made in chemically induced CDK degradation and how these studies can contribute to CDK inhibitor design. Additionally, fragment-based drug discovery can be utilised to identify small molecules that bind to allosteric sites on the CDK surface employing interactions which mimic those of native protein-protein interactions. These recent structural advances in CDK inhibitor mechanisms and in chemical probes which do not occupy the orthosteric ATP binding site can provide important insights for targeted CDK therapies.

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Protein Kinases as Potential Targets Contribute to the Development of Agrochemicals

Using agrochemicals against pest insects, fungi, and weeds plays a major part in maintaining and improving crop yields, which helps to solve the issue of food security. Due to the limited targets and resistance of agrochemicals, protein kinases are regarded as attractive potential targets to develop new agrochemicals. Recently, a lot of investigations have shown the extension of agrochemicals by targeting protein kinases, implying an increasing concern for this kind of method. However, few people have summarized and discussed the targetability of protein kinases contributing to the development of agrochemicals. In this work, we introduce the research on protein kinases as potential targets used in crop protection and discuss the prospects of protein kinases in the field of agrochemical development. This study may not only provide guidance for the contribution of protein kinases to the development of agrochemicals but also help nonprofessionals such as students learn and understand the role of protein kinases quickly.

Development of urease inhibitors by fragment-based dynamic combinatorial chemistry

In this study, fragment-based dynamic combinatorial chemistry (DCC) was explored for the development of novel urease inhibitors. Based on a rationally designed fragment, two iteratively evolved dynamic combinatorial libraries (DCLs) were generated and screened in the presence of urease template. The best ligand identified revealed not only strong urease inhibition but also low cytotoxicity. Additionally, possible inhibitory mechanism was elucidated in the binding kinetic study and docking simulation.

Small-Molecule Inhibitors Targeting FEN1 for Cancer Therapy

DNA damage repair plays a key role in maintaining genomic stability and integrity. Flap endonuclease 1 (FEN1) is a core protein in the base excision repair (BER) pathway and participates in Okazaki fragment maturation during DNA replication. Several studies have implicated FEN1 in the regulation of other DNA repair pathways, including homologous recombination repair (HRR) and non-homologous end joining (NHEJ). Abnormal expression or mutation of FEN1 in cells can cause a series of pathological responses, leading to various diseases, including cancers. Moreover, overexpression of FEN1 contributes to drug resistance in several types of cancers. All this supports the hypothesis that FEN1 could be a therapeutic target for cancer treatment. Targeting FEN1 has been verified as an effective strategy in mono or combined treatment of cancer. Small-molecule compounds targeting FEN1 have also been developed and detected in cancer regression. In this review, we summarize the recent development of small-molecule inhibitors targeting FEN1 in recent years, thereby expanding their therapeutic potential and application.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

Chemical Reactivity and Optical and Pharmacokinetics Studies of 14 Multikinase Inhibitors and Their Docking Interactions Toward ACK1 for Precision Oncology

Activated Cdc42-associated kinase 1 (ACK1/TNK2) has a significant role in cell endocytosis, survival, proliferation, and migration. Mutations in ACK1 are closely associated with the occurrence and development of cancers. In this work, a conceptual density functional theory (CDFT)-based computational peptidology (CDFT-CP) method is used to study the chemical reactivity of 14 multikinase inhibitors. Optical properties of these inhibitors are studied by time-dependent density functional theory (TDDFT). Various biological and pharmacokinetic parameters are studied by Osiris, Molinspiration, and BOILED-Egg in SwissADME software tools. Physicochemical and biopharmaceutical (PCB), Salmonella typhimurium reverse mutation assay (AMES) mutagenicity, toxicity, and risk prediction are estimated by Simulations plus ADMET Predictor 10.2 software. MD simulations for an active model of ACK1 is carried out by the CABS-flex 2.0 web server, and potential binding pockets for ACK1 are searched using the PrankWeb server. SwissTargetPrediction is used to predict the potential targets for the multikinase inhibitors. Docking studies are carried out for ACK1–multikinase inhibitors using Autodock 4.2 software. Noncovalent interactions for ACK1–multikinase inhibitor complexes are studied using the Protein–Ligand Interaction Profiler (PLIP) server. Results indicated higher binding affinities and strong noncovalent interactions in ACK1–multikinase inhibitor complexes.

Methods for the Discovery and Identification of Small Molecules Targeting Oxidative Stress-Related Protein–Protein Interactions: An Update

The oxidative stress response pathway is one of the hotspots of current pharmaceutical research. Many proteins involved in these pathways work through protein–protein interactions (PPIs). Hence, targeting PPI to develop drugs for an oxidative stress response is a promising strategy. In recent years, small molecules targeting protein–protein interactions (PPIs), which provide efficient methods for drug discovery, are being investigated by an increasing number of studies. However, unlike the enzyme–ligand binding mode, PPIs usually exhibit large and dynamic binding interfaces, which raise additional challenges for the discovery and optimization of small molecules and for the biochemical techniques used to screen compounds and study structure–activity relationships (SARs). Currently, multiple types of PPIs have been clustered into different classes, which make it difficult to design stationary methods for small molecules. Deficient experimental methods are plaguing medicinal chemists and are becoming a major challenge in the discovery of PPI inhibitors. In this review, we present current methods that are specifically used in the discovery and identification of small molecules that target oxidative stress-related PPIs, including proximity-based, affinity-based, competition-based, structure-guided, and function-based methods. Our aim is to introduce feasible methods and their characteristics that are implemented in the discovery of small molecules for different types of PPIs. For each of these methods, we highlight successful examples of PPI inhibitors associated with oxidative stress to illustrate the strategies and provide insights for further design.

Small-Molecule Lead-Finding Trends across the Roche and Genentech Research Organizations

The origin of small-molecule leads that were pursued across the independent research organizations Roche and Genentech from 2009 to 2020 is described. The identified chemical series are derived from a variety of lead-finding methods, which include public information, high-throughput screening (both full file and focused), fragment-based design, DNA-encoded library technology, use of legacy internal data, in-licensing, and de novo design (often structure-based). The translation of the lead series into in vivo tool compounds and development candidates is discussed as are the associated biological target classes and corresponding therapeutic areas. These analyses identify important trends regarding the various lead-finding approaches, which will likely impact their future application in the Roche and Genentech research groups. They also highlight commonalities and differences across the two independent research organizations. Several caveats associated with the employed data collection and analysis methodologies are included to enhance the interpretation of the presented information.

Fragment-to-Lead Medicinal Chemistry Publications in 2020

Fragment-based drug discovery (FBDD) continues to evolve and make an impact in the pharmaceutical sciences. We summarize successful fragment-to-lead studies that were published in 2020. Having systematically analyzed annual scientific outputs since 2015, we discuss trends and best practices in terms of fragment libraries, target proteins, screening technologies, hit-optimization strategies, and the properties of hit fragments and the leads resulting from them. As well as the tabulated Fragment-to-Lead (F2L) programs, our 2020 literature review identifies several trends and innovations that promise to further increase the success of FBDD. These include developing structurally novel screening fragments, improving fragment-screening technologies, using new computer-aided design and virtual screening approaches, and combining FBDD with other innovative drug-discovery technologies.

Repurposing FDA-approved Drugs Targeting SARS-CoV2 3CLpro: a study by applying Virtual Screening, Molecular Dynamics, MM-PBSA Calculations and Covalent Docking

Background:

Since the end of 2019, the etiologic agent SAR-CoV-2 responsible for one of the most significant epidemics in history has caused severe global economic, social, and health damages. The drug repurposing approach and application of Structure-based Drug Discovery (SBDD) using in silico techniques are increasingly frequent, leading to the identification of several molecules that may represent promising potential.

Method:

In this context, here we use in silico methods of virtual screening (VS), pharmacophore modeling (PM), and fragment-based drug design (FBDD), in addition to molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM -PBSA) calculations, and covalent docking (CD) for the identification of potential treatments against SARS-CoV-2. We initially validated the docking protocol followed by VS in 1,613 FDA-approved drugs obtained from the ZINC database. Thus, we identified 15 top hits, of which three of them were selected for further simulations. In parallel, for the compounds with a fit score value ≤ of 30, we performed the FBDD protocol, where we designed 12 compounds

Result:

By applying a PM protocol in the ZINC database, we identified three promising drug candidates. Then, the 9 top hits were evaluated in simulations of MD, MM-PBSA, and CD. Subsequently, MD showed that all identified hits showed stability at the active site without significant changes in the protein's structural integrity, as evidenced by the RMSD, RMSF, Rg, SASA graphics. They also showed interactions with the catalytic dyad (His41 and Cys145) and other essential residues for activity (Glu166 and Gln189) and high affinity for MM-PBSA, with possible covalent inhibition mechanism.

Conclution:

Finally, our protocol helped identify potential compounds wherein ZINC896717 (Zafirlukast), ZINC1546066 (Erlotinib), and ZINC1554274 (Rilpivirine) were more promising and could be explored in vitro, in vivo, and clinical trials to prove their potential as antiviral agents.

Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities

New chemical entities (NCEs) such as small molecules and antibody-drug conjugates have strong binding affinity for biological targets, which provide deep insights into structure-specific interactions for the design of future drugs. As structures of drugs increase in complexity, the importance of computational predictions comes into sharp focus. Knowledge of various computational tools enables us to predict the molecular properties, toxicity, and biological efficacy of the drugs and help the medicinal chemists to discover new drugs more efficiently. Newly approved drugs have higher affinities for proteins and nucleic acids and are applied for the treatment of human diseases. We have carried out the computational studies of 21 such NCEs, specifically small molecules and antibody-drug conjugates, and studied the biological efficacy of these drugs. Their bioactivity score and molecular and pharmacokinetic properties were evaluated using online computer software programs, viz., Molinspiration and Osiris Property Explorer. The SwissTargetPrediction tool was used for the efficient prediction of protein targets for the NCEs. The results indicated higher stability for the drug complexes due to a larger HOMO-LUMO gap. A high electrophilicity index reflects good electrophilic behavior and high reactivity of the drugs. Lipinski's ''rule of five'' indicated that most of the drug complexes are likely to be orally active. These drugs also showed non-mutagenic, non-tumorigenic, non-irritant, and non-effective reproductive behavior. We hope that these studies will provide an insight into molecular recognition and definitely help the medicinal chemists to design new drugs in future.