Discovery and Anticancer Screening of Novel Oxindole-Based Derivative Bearing Pyridyl Group as Potent and Selective Dual FLT3/CDK2 Kinase Inhibitor

Protein kinases regulate cellular activities and make up over 60% of oncoproteins and proto-oncoproteins. Among these kinases, FLT3 is a member of class III receptor tyrosine kinase family which is abundantly expressed in individuals with acute leukemia. Our previous oxindole-based hit has a particular affinity toward FLT3 (IC50 = 2.49 μM) and has demonstrated selectivity towards FLT3 ITD-mutated MV4-11 AML cells, with an IC50 of 4.3 μM. By utilizing the scaffold of the previous hit, sixteen new compounds were synthesized and screened against NCI-60 human cancer cell lines. This leads to the discovery of a potent antiproliferative compound, namely 5l, with an average GI50 value against leukemia and colon cancer subpanels equalling 3.39 and 5.97 µM, respectively. Screening against a specific set of 10 kinases that are associated with carcinogenesis indicates that compound 5l has a potent FLT3 inhibition (IC50 = 36.21 ± 1.07 nM). Remarkably, compound 5l was three times more effective as a CDK2 inhibitor (IC50 = 8.17 ± 0.32 nM) compared to sunitinib (IC50 = 27.90 ± 1.80 nM). Compound 5l was further analyzed by means of docking and molecular dynamics simulation for CDK2 and FLT3 active sites which provided a rational for the observed strong inhibition of kinases. These results suggest a novel structural scaffold candidate that simultaneously inhibits CDK2 and FLT3 and gives encouragement for further development as a potential therapeutic for leukemia and colon cancer.

Comprehensive Research on Druggable Proteins: From PSSM to Pre-Trained Language Models

Identification of druggable proteins can greatly reduce the cost of discovering new potential drugs. Traditional experimental approaches to exploring these proteins are often costly, slow, and labor-intensive, making them impractical for large-scale research. In response, recent decades have seen a rise in computational methods. These alternatives support drug discovery by creating advanced predictive models. In this study, we proposed a fast and precise classifier for the identification of druggable proteins using a protein language model (PLM) with fine-tuned evolutionary scale modeling 2 (ESM-2) embeddings, achieving 95.11% accuracy on the benchmark dataset. Furthermore, we made a careful comparison to examine the predictive abilities of ESM-2 embeddings and position-specific scoring matrix (PSSM) features by using the same classifiers. The results suggest that ESM-2 embeddings outperformed PSSM features in terms of accuracy and efficiency. Recognizing the potential of language models, we also developed an end-to-end model based on the generative pre-trained transformers 2 (GPT-2) with modifications. To our knowledge, this is the first time a large language model (LLM) GPT-2 has been deployed for the recognition of druggable proteins. Additionally, a more up-to-date dataset, known as Pharos, was adopted to further validate the performance of the proposed model.

Shaping Chirality via Stereoselective, Organocatalytic [4+2] Cycloadditions involving Heterocyclic ortho-Quinodimethanes

Polycyclic compounds bearing a complex heterocyclic core such as an aromatic heterocycle "fused" with one or more functionalized rings, are widespread leading molecules in the domain of synthetic organic chemistry and pharmaceuticals. Although many synthetic methodologies have been devised to access achiral, fused heteroaromatic scaffolds, or related chiral variants adorned with out-of-cycle stereogenic elements, equally efficient strategies to afford chiral heterocycles featuring in-cycle stereocenters, exist to a lesser extent and presently represent a growing field of investigation. The mild, organocatalytic generation of elusive ortho-quinodimethane intermediates (oQDMs), derived from suitable heteroaromatic carbonyl- or carbonyl-like pronucleophiles has recently proved successful in the synthesis of such peculiar chiral architectures via stereoselective [4+2] cycloadditions. This review provides an overview of the most important advances attained in this field over the last decade.

In silico design of novel CDK2 inhibitors through QSAR, ADMET, molecular docking and molecular dynamics simulation studies

The present study aims to investigate about the quantitative structure-activity relationship (QSAR) of a series of Thiazole derivatives reported as anticancer agents (hepatocellular carcinoma), using principally the electronic descriptors calculated by the DFT method and by applying the multiple linear regression method. The developed model showed good statistical parameters (R2 = 0.725, R2adj = 0.653, MSE = 0.060, R2test = 0.827, Q2cv = 0.536). The energy EHOMO orbital, electronic energy (TE), shape coefficient (I), number of rotatable bonds (NROT), and index of refraction (n) were revealed to be the main descriptors influencing the anti-cancer activity. Further, new Thiazole derivatives have been designed and their activities and pharmacokinetic properties have been predicted using the validated QSAR model. The designed molecules were then assessed to molecular docking (MD), and molecular dynamic (MDs) simulation accompanied by the calculation of the binding affinity using MMPBSA script according to 100 ns a simulation trajectory, to study both their affinity and their stability towards CDK2 as a target protein for the cancer disease treatment. This research concluded with the identification of four new CDK2 inhibitors which are A1, A3, A5, and A6 showing good pharmacokinetic properties. The MDs results revealed that the newly designed compound A5 remained stable in the active center of the discovered CDK2 protein, indicating its potential as a novel inhibitor for the treatment of hepatocellular carcinoma. The current findings may eventually contribute to the development of robust CDK2 inhibitors in the future.Communicated by Ramaswamy H. Sarma.

Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data

Machine learning is a popular technique to predict the retention times of molecules based on descriptors. Descriptors and associated labels (e.g., retention times) of a set of molecules can be used to train a machine learning algorithm. However, descriptors are fixed molecular features which are not necessarily optimized for the given machine learning problem (e.g., to predict retention times). Recent advances in molecular machine learning make use of so-called graph convolutional networks (GCNs) to learn molecular representations from atoms and their bonds to adjacent atoms to optimize the molecular representation for the given problem. In this study, two GCNs were implemented to predict the retention times of molecules for three different chromatographic data sets and compared to seven benchmarks (including two state-of-the art machine learning models). Additionally, saliency maps were computed from trained GCNs to better interpret the importance of certain molecular sub-structures in the data sets. Based on the overall observations of this study, the GCNs performed better than all benchmarks, either significantly outperforming them (5-25% lower mean absolute error) or performing similar to them (<5% difference). Saliency maps revealed a significant difference in molecular sub-structures that are important for predictions of different chromatographic data sets (reversed-phase liquid chromatography vs hydrophilic interaction liquid chromatography).

Ahp-Cyclodepsipeptide Inhibitors of Elastase: Lyngbyastatin 7 Stability, Scalable Synthesis, and Focused Library Analysis

Due to the potency and selectivity of lyngbyastatin 7 in inhibiting neutrophil elastase, a serine protease involved in numerous diseases, this cyclodepsipeptide was considered as a promising lead and subjected to further developmental studies. Lyngbyastatin 7 displayed a favorable serum and microsomal stability profile. The large-scale synthesis of key building blocks was performed on gram scale with improved yields and simplified purification procedures. To tailor the complex structure, define the minimal pharmacophore, and modulate the physicochemical properties of the lead scaffold, the first pilot library of analogues was designed and synthesized for structure-activity relationship studies. We uncovered the essential role of the side chain, indicating that the minimal structural requirements for elastase inhibition extended beyond the 3-amino-6-hydroxy-2-piperidone (Ahp) and 2-aminobutenoic acid (Abu) moieties conventionally known to convey antiprotease activity and elastase selectivity, respectively. Our studies will facilitate the design and development of this class of elastase inhibitors.

Cardiovascular disease models: A game changing paradigm in drug discovery and screening

Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.

Industrial medicinal chemistry insights: neuroscience hit generation at Janssen

The role of medicinal chemistry has changed over the past 10 years. Chemistry had become one step in a process; funneling the output of high-throughput screening (HTS) on to the next stage. The goal to identify the ideal clinical compound remains, but the means to achieve this have changed. Modern medicinal chemistry is responsible for integrating innovation throughout early drug discovery, including new screening paradigms, computational approaches, novel synthetic chemistry, gene-family screening, investigating routes of delivery, and so on. In this Foundation Review, we show how a successful medicinal chemistry team has a broad impact and requires multidisciplinary expertise in these areas.

Synthesis and Biopharmaceutical Evaluation of Imatinib Analogues Featuring Unusual Structural Motifs

A convenient synthesis of imatinib, a potent inhibitor of ABL1 kinase and widely prescribed drug for the treatment of a variety of leukemias, was devised and applied to the construction of a series of novel imatinib analogues featuring a number of non-aromatic structural motifs in place of the parent molecule's phenyl moiety. These analogues were subsequently evaluated for their biopharmaceutical properties (e.g., ABL1 kinase inhibitory activity, cytotoxicity). The bicyclo[1.1.1]pentane- and cubane-containing analogues were found to possess higher themodynamic solubility, whereas cubane- and cyclohexyl-containing analogues exhibited the highest inhibitory activity against ABL1 kinase and the most potent cytotoxicity values against cancer cell lines K562 and SUP-B15. Molecular modeling was employed to rationalize the weak activity of the compounds against ABL1 kinase, and it is likely that the observed cytotoxicity of these agents arises through off-target effects.

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.

Recent progress in direct introduction of fluorinated groups on alkenes and alkynes by means of C-H bond functionalization

The direct introduction of fluorine and fluorinated building blocks has recently attracted a lot of attention and particularly the direct functionalization of alkenes and alkynes. This review will highlight the major progress recently made in that field, with a focus on photocatalyzed transformations, base-promoted processes, and transition metal-catalyzed functionalization of alkenes and alkynes. Special attention will be paid to explanations of the reaction mechanisms.