Optimization Of Small-molecule Inhibitors Research Articles

Computational analysis of PD-L1 dimerization mechanism induced by small molecules and potential dynamical properties

The design of small molecule inhibitors that target the programmed death ligand-1 (PD-L1) is a forefront issue in immune checkpoint blocking therapy. Small-molecule inhibitors have been shown to exert therapeutic effects by inducing dimerization of the PD-L1 protein, however, the specific mechanisms underlying this dimerization process remain largely unexplored. Furthermore, there is a notable lack of comparative studies examining the binding modes of structurally diverse inhibitors. In view of the research gaps, this work employed molecular dynamics simulations to meticulously examine the interactions between two distinct types of inhibitors and PD-L1 in both monomeric and dimeric forms, and predicted the dimerization mechanism. The results revealed that inhibitors initially bind to a PD-L1 monomer, subsequently attracting another monomer to form a dimer. Notably, symmetric inhibitors observed superior binding efficiency compared to other inhibitors. Key residues, including Ile54, Tyr56, Met115 and Tyr123 played a leading role in binding. Structurally, symmetric inhibitors were capable of thoroughly engaging the binding pocket, promoting a more symmetrical formation of PD-L1 dimers. Furthermore, symmetric inhibitors formed more extensive hydrophobic interactions with protein residues. The insights garnered from this research are expected to significantly contribute to the rational design and optimization of small molecule inhibitors targeting PD-L1.

Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints.

Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learning methodology, it is becoming more tractable to elucidate the structural basis of drug potency. However, the applicability of machine learning models to drug design is limited by the interpretability of the resulting models in terms of feature importance. Here, we take advantage of the large number of available inhibitor-bound HIV-1 protease structures and associated potencies to evaluate inhibitor diversity and machine learning models to predict ligand affinity. First, using a hierarchical clustering approach, we grouped HIV-1 protease inhibitors and identified distinct core structures. Explicit features including protein-ligand interactions were extracted from high-resolution cocrystal structures as 3D-based fingerprints. We found that a gradient boosting machine learning model with this explicit feature attribution can predict binding affinity with high accuracy. Finally, Shapley values were derived to explain local feature importance. We found specific van der Waals (vdW) interactions of key protein residues are pivotal for the predicted potency. Protein-specific and interpretable prediction models can guide the optimization of many small molecule drugs for improved potency.

Inhibitors of the Diadenosine Tetraphosphate Phosphorylase Rv2613c of Mycobacterium tuberculosis.

The intracellular concentration of diadenosine tetraphospate (Ap4A) increases upon exposure to stress conditions. Despite being discovered over 50 years ago, the cellular functions of Ap4A are still enigmatic. If and how the varied Ap4A is a signal and involved in the signaling pathways leading to an appropriate cellular response remain to be discovered. Because the turnover of Ap4A by Ap4A cleaving enzymes is rapid, small molecule inhibitors for these enzymes would provide tools for the more detailed study of the role of Ap4A. Here, we describe the development of a high-throughput screening assay based on a fluorogenic Ap4A substrate for the identification and optimization of small molecule inhibitors for Ap4A cleaving enzymes. As proof-of-concept we screened a library of over 42 000 compounds toward their inhibitory activity against the Ap4A phosphorylase (Rv2613c) of Mycobacterium tuberculosis (Mtb). A sulfanylacrylonitril derivative with an IC50 of 260 ± 50 nM in vitro was identified. Multiple derivatives were synthesized to further optimize their properties with respect to their in vitro IC50 values and their cytotoxicity against human cells (HeLa). In addition, we selected two hits to study their antimycobacterial activity against virulent Mtb to show that they might be candidates for further development of antimycobacterial agents against multidrug-resistant Mtb.

Abstract 3898: Structure-based optimization of small molecule inhibitors of the protein-protein interaction between menin and mixed lineage leukemia (MLL)

Abstract Chromosomal translocations that affect the Mixed Lineage Leukemia (MLL) gene result in acute myeloid and lymphoid leukemias. Fusion of MLL with one of 60 different partner genes generates MLL fusion proteins which lead to enhanced cell proliferation, up-regulation of Hoxa9 and Meis1 genes and block hematopoietic differentiation, ultimately leading to acute leukemia. Importantly, the N-terminal region of MLL is retained in all MLL fusion proteins and represents the interaction site of menin, a critical oncogenic cofactor for MLL fusion proteins. As a result, inhibition of the menin-MLL interaction should abrogate the development and progression of MLL leukemia. Therefore, disruption of the menin-MLL interaction with small molecule inhibitors might represent a therapeutic strategy for patients harboring MLL-rearrangements. The development of potent small molecule inhibitors of protein-protein interactions with optimized drug-like properties represents a challenging task in the lead optimization process. Here, we employed structure-based design with extensive medicinal chemistry efforts to optimize the thienopyrimidine class of menin-MLL inhibitors represented by the MI-136 compound. Our efforts resulted in development of MI-538, which represents the most potent small molecule inhibitor of the menin-MLL interaction developed to date. MI-538, which binds to menin with a Kd of 6.5 nM demonstrated superior cellular activity in MLL-AF9 transformed leukemic cells with a GI50 = 83 nM. In a mouse xenograft model utilizing MV4:11 human MLL leukemia cells, treatment with MI-538 significantly reduced tumor growth. Overall, systematic exploration of substituents on MI-136 led to the identification of MI-538 with improved activity, selectivity, polarity and pharmacokinetic profile, making it suitable for in vivo studies. Interestingly, we found that simultaneous incorporation of multiple substituents optimized for each site on the cyanoindole ring of MI-136 does not always positively affect the activity or drug-like properties of these compounds. This study demonstrates challenges in optimizing inhibitors of protein-protein interaction for potential therapeutic applications. Citation Format: Jonathan Pollock, Dmitry Borkin, Katarzyna Kempinska, Trupta Purohit, Xiaoqin Li, Bo Wen, Ting Zhao, Hongzhi Miao, Shirish Shukla, Miao He, Duxin Sun, Tomasz Cierpicki, Jolanta Grembecka. Structure-based optimization of small molecule inhibitors of the protein-protein interaction between menin and mixed lineage leukemia (MLL). [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3898.

Property Focused Structure-Based Optimization of Small Molecule Inhibitors of the Protein-Protein Interaction between Menin and Mixed Lineage Leukemia (MLL).

Development of potent small molecule inhibitors of protein-protein interactions with optimized druglike properties represents a challenging task in lead optimization process. Here, we report synthesis and structure-based optimization of new thienopyrimidine class of compounds, which block the protein-protein interaction between menin and MLL fusion proteins that plays an important role in acute leukemias with MLL translocations. We performed simultaneous optimization of both activity and druglike properties through systematic exploration of substituents introduced to the indole ring of lead compound 1 (MI-136) to identify compounds suitable for in vivo studies in mice. This work resulted in the identification of compound 27 (MI-538), which showed significantly increased activity, selectivity, polarity, and pharmacokinetic profile over 1 and demonstrated a pronounced effect in a mouse model of MLL leukemia. This study, which reports detailed structure-activity and structure-property relationships for the menin-MLL inhibitors, demonstrates challenges in optimizing inhibitors of protein-protein interactions for potential therapeutic applications.

Application of cultured human mast cells (CHMC) for the design and structure-activity relationship of IgE-mediated mast cell activation inhibitors.

Here we report the optimization of small molecule inhibitors of human mast cell degranulation via anti-IgE-mediated tryptase release following cross-linking and activation of IgE-loaded FcεR1 receptors. The compounds are selective upstream inhibitors of FcεR1-dependent human mast cell degranulation and proved to be devoid of activity in downstream ionomycin mediated degranulation. Structure-activity relationship (SAR) leading to compound 26 is outlined.

Molecular Interaction Fields and 3D-QSAR Studies of p53−MDM2 Inhibitors Suggest Additional Features of Ligand−Target Interaction

The design and optimization of small molecule inhibitors of the murine double minute clone 2-p53 (p53-MDM2) interaction has attracted a great deal of interest as a way to novel anticancer therapies. Herein we report 3D-QSAR studies of 41 small molecule inhibitors based on the use of molecular interaction fields and docking experiments as part of an approach to generating predictive models of MDM2 affinity and shedding further light on the structural elements of the ligand-target interaction. These studies have yielded predictive models explaining much of the variance of the 41 compound training set and satisfactorily predicting with 75% success an external test set of 36 compounds. Not surprisingly, and in full agreement with previous data, inspection of the 3D-QSAR coefficients reveals that the major driving force for potent inhibition is given by the hydrophobic interaction between the inhibitors and the p53 binding cleft of MDM2. More surprisingly, and challenging previous suggestions, the projection of the 3D-QSAR coefficients back onto the experimental structures of MDM2 provides an intriguing hypothesis concerning an active role played by the N-terminal region of MDM2 in ligand binding.