Lead Optimization Research Articles

New Platform for Label-Free, Proximal Cellular Pharmacodynamic Assays: Identification of Glutaminase Inhibitors Using Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry.

Cellular pharmacodynamic assays are crucial aspects of lead optimization programs in drug discovery. These assays are sometimes difficult to develop, oftentimes distal from the target and frequently low throughput, which necessitates their incorporation in the drug discovery funnel later than desired. The earlier direct pharmacodynamic modulation of a target can be established, the fewer resources are wasted on compounds that are acting via an off-target mechanism. Mass spectrometry is a versatile tool that is often used for direct, proximal cellular pharmacodynamic assay analysis, but liquid chromatography-mass spectrometry methods are low throughput and are unable to fully support structure-activity relationship efforts in early medicinal chemistry programs. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is an ambient ionization method amenable to high-throughput cellular assays, capable of diverse analyte detection, ambient and rapid laser sampling processes, and low cross-contamination. Here, we demonstrate the capability of IR-MALDESI for the detection of diverse analytes directly from cells and report the development of a high-throughput, label-free, proximal cellular pharmacodynamic assay using IR-MALDESI for the discovery of glutaminase inhibitors and a biochemical assay for hit confirmation. We demonstrate the throughput with a ∼100,000-compound cellular screen. Hits from the screening were confirmed by retesting in dose-response with mass spectrometry-based cellular and biochemical assays. A similar workflow can be applied to other targets with minimal modifications, which will speed up the discovery of cell active lead series and minimize wasted chemistry resources on off-target mechanisms.

Lead Optimization of Androgen Receptor-HSP27 Disrupting Agents in Glioblastoma.

Glioblastoma (GBM) is the most common malignant brain tumor with poor prognosis under the current standard treatment. It is critical to develop new approaches to selectively battle the disease. GBM sex differences suggest that an androgen receptor (AR) is a potential therapeutic target to treat AR-overexpressed GBM. Heat shock 27 kDa protein (HSP27) is a well-documented chaperone protein that stabilizes AR. Inhibition of HSP27 leads to AR degradation, indicating that HSP27 inhibitors could suppress AR activity in GBM. We have identified a lead HSP27 inhibitor that could induce AR degradation. Lead optimization resulted with two new derivatives (compounds 4 and 26) showing potent anti-GBM activity and improved drug distribution in comparison to the lead compound. Compounds 4 and 6 exhibit IC50s of 35 and 23 nM, respectively, to inhibit cell proliferation and also show significant activity to decrease the tumor growth in vivo.

HSF1 Pathway Inhibitor Clinical Candidate (CCT361814/NXP800) Developed from a Phenotypic Screen as a Potential Treatment for Refractory Ovarian Cancer and Other Malignancies.

CCT251236 1, a potent chemical probe, was previously developed from a cell-based phenotypic high-throughput screen (HTS) to discover inhibitors of transcription mediated by HSF1, a transcription factor that supports malignancy. Owing to its activity against models of refractory human ovarian cancer, 1 was progressed into lead optimization. The reduction of P-glycoprotein efflux became a focus of early compound optimization; central ring halogen substitution was demonstrated by matched molecular pair analysis to be an effective strategy to mitigate this liability. Further multiparameter optimization led to the design of the clinical candidate, CCT361814/NXP800 22, a potent and orally bioavailable fluorobisamide, which caused tumor regression in a human ovarian adenocarcinoma xenograft model with on-pathway biomarker modulation and a clean in vitro safety profile. Following its favorable dose prediction to human, 22 has now progressed to phase 1 clinical trial as a potential future treatment for refractory ovarian cancer and other malignancies.

Abstract 2024: High resolution limited proteolysis (HR-LiP), a novel structural proteomics approach for the prediction of small molecule-protein binding events

Abstract High resolution profiling of drug-protein interactions and binding mechanisms remains a major hurdle during lead selection and optimization. A key milestone in structure-based drug design is compound binding site identification and characterization. Structure-activity relationship (SAR) studies traditionally utilize time and cost intensive techniques such as nuclear magnetic resonance (NMR), x-ray crystallography (X-ray) and cryo-electron microscopy (cryo-EM). To date, hydrogen-deuterium exchange (HDX) is the only mass spectrometry-based approach that has been extensively utilized. Further, SAR studies are often complicated by protein size (large proteins and/or oligomers) and location (membrane proteins), which can lead to protocol adaptations that can introduce artifacts. High resolution limited proteolysis (HR-LiP) is a high-throughput, high-resolution approach based on LiP-Quant that utilizes peptide-level resolution to characterize drug-protein interactions. Two well-characterized drug target proteins, bromodomain-containing protein 4 (BRD4) and transitional endoplasmic reticulum ATPase (VCP), were selected for analysis as they represent both difficult targets for structural biology (size and oligomerization respectively). BRD4 was targeted with a well characterized inhibitor (JQ1), while VCP was investigated for binding with a known autophagy activator. Using HR-LiP we identify the binding site of the BRD4 inhibitor JQ1 in the full-length protein, which is typically too large to be used directly in with conventional methods. Further, we confirm binding of the autophagy activator to VCP at a site known to increase VCP activity. For both compounds tested, our data are in good accordance with orthogonal data obtained by other structural protein approaches including HDX-MS and “click” chemistry-based chemoproteomics. We demonstrate that HR-LiP can be used to dissect small molecule-protein binding events, including compound binding site prediction for protein targets classically considered to be difficult and time intensive. Further, we show that this data can be used to shed light on the biological mechanisms driving a phenotypic response. Given its biological power, broad applicability and ease of implementation, we envision the use of HR-LiP as a routine approach for target validation and lead optimization in small molecule drug discovery pipelines. Citation Format: Nigel Beaton, Roland Bruderer, Yuehan Feng, Jagat Adhikari, Ivan Cornella-Taracido, Lukas Reiter. High resolution limited proteolysis (HR-LiP), a novel structural proteomics approach for the prediction of small molecule-protein binding events [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2024.

Abstract 4058: In vitro binding, efficacy and safety studies to support engineered T cell therapies

Abstract Engineered T cell therapies such as Chimeric Antigen Receptor (CAR) T cells and T cell receptor (TCR)-engineered T cells have emerged as a promising cancer therapy. To date, four anti-CD19 CAR-T and two anti-BCMA CAR-T products have been approved by the FDA for the treatment of hematological malignancies. Many more T cell therapy products are currently being explored, directed towards both liquid and solid tumors as well as for other clinical indications. High-quality and robust in vitro and in vivo assays are essential for the discovery and characterization of lead T cell therapy products. Furthermore, despite demonstrating therapeutically successful, the further development of T cell immunotherapies has been hindered by safety concerns. Selected target antigens might be expressed in healthy tissues or engineered T cells may non-specifically bind to antigens in healthy tissues, potentially resulting in severe side effects. In addition, the random integration of the CAR- or TCR-encoding DNA cassettes in the host cell genome has the potential risk of causing insertional mutagenesis and may contribute to oncogenic transformation of the T cells. The aim of this study was to develop several in vitro assays for the assessment of target cell binding, efficacy and safety of T cell therapies using CAR-T cells targeting the Human Epidermal growth factor Receptor 2 (HER2) as a model system. The z-Movi cell avidity analyzer was used to study the binding strength between the CAR-T cells and target tumor cells. Cytotoxicity co-culture assays were developed using increasing effector:target cells ratios and an impedance-based readout to quantify the viability of HER2-positive and -negative cancer cell lines in real-time, to confirm the activity and selectivity of the HER2-CAR-T cells. Furthermore, co-culture assays were also developed for a variety of primary or iPSC-derived healthy human cells (representing various tissues) to assess potential off-tumor effects of the HER2-CAR-T cells against healthy cells. In addition, an oncogenicity assay was developed to quantify the survival and proliferation of the HER2-CAR-T cells in the absence and presence of cytokines by flow cytometry, to determine whether the genomic editing of the T cells affected their cytokine-dependency. In conclusion, Charles River Laboratories developed several in vitro assays for the preclinical assessment of T cell therapy binding, efficacy, potency, specificity and safety to aid early-stage lead discovery, optimization and development, and to support Investigational New Drug (IND) applications. Charles River has the capabilities to support a full CAR/TCR program from inception to IND filing. Citation Format: Sophie Vermond, Benita Quist, Monique Hazenoot, Rene McLaughlin, David Cobeta Lopez, Namrata Jayanth, Folkert Verkaar, Omar Aziz, Maria LH Vlaming, Sabrina de Munnik, Gemma Moiset. In vitro binding, efficacy and safety studies to support engineered T cell therapies. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4058.

Abstract 519: A novel Skp1 inhibitor has potent preclinical efficacy against metastatic castration-resistant prostate cancer

Abstract Purpose: Despite numerous therapeutic modalities, including chemotherapy and next-generation androgen deprivation therapy, metastatic castration-resistant prostate cancer (mCRPC) remains a dreadful evolution and still bears a poor prognosis for most patients who develop metastases to bones and other organs, which have no cure. It is urgent to identify new molecular targets and develop novel therapeutic agents against lethal mCRPC. Methods: We developed a novel small-molecule anticancer compound named GH501 via a “molecular hybridization” approach. The in vitro cytotoxicity of GH501 was evaluated in the NCI-60 human cancer cell panel and human mCRPC cell lines. Molecular targets of GH501 were investigated using in silico docking, biolayer interferometry, cellular thermal shift assay, and other molecular and cellular approaches. The in vivo anticancer efficacy of GH501 against mCRPC was evaluated in clinically-relevant xenograft models. Results: GH501 effectively inhibited the in vitro proliferation of NCI-60 human cancer cell lines and established mCRPC cell lines, including African American prostate cancer (PCa) cells, at nanomolar potency by inducing cell cycle arrest and apoptosis. Mechanistically, GH501 might bind S-phase kinase-associated protein 1 (Skp1) and disrupt the physical interaction between Skp1 and S-phase kinase-associated protein 2 (Skp2), thereby affecting multiple oncogenic signals implicated in mCRPC progression. RNA-Seq analyses indicated that GH501 treatment in C4-2 cells significantly inhibited E2F-, G2/M checkpoint, c-Myc- and β-catenin-regulated genes, androgen receptor (AR) transcription, androgen-responsive genes, and enhancer of zeste homolog 2 (EZH2)-activated genes. Proteomic studies identified 109 proteins upregulated and 154 proteins downregulated significantly by GH501 in CRW22Rv1 cells. The top physiological processes and signaling pathways affected by GH501 included cell cycle, autophagy, steroid biosynthesis, DNA replication, p53, AMP-activated protein kinase (AMPK), and mitogen-activated protein kinase (MAPK). As a lead compound, GH501 had excellent preclinical safety. In animal studies, GH501 monotherapy effectively inhibited the skeletal and subcutaneous growth of four mCRPC xenografts with heterogeneous genetic backgrounds. Conclusion: These results support that pharmacological interruption of Skp1-Skp2 interaction is a promising therapeutic strategy for mCRPC. To our knowledge, GH501 is the first synthetic Skp1 inhibitor with a unique chemical structure that can serve as the basis for lead optimization and development. Citation Format: Xin Li, Kenza Mamouni, Rui Zhao, Yifei Wu, Zhong-Ru Xie, Giuseppe Sautto, Degang Liu, Nathan Bowen, Alira Danaher, Lajos Gera, Daqing Wu. A novel Skp1 inhibitor has potent preclinical efficacy against metastatic castration-resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 519.

Abstract 1537: DRIVE-Biologics: All the steps from discovery to development of novel biological entities

Abstract Biologics first revolutionized cancer treatment in the late 1900s with the approval of rituximab and trastuzumab, two monoclonal antibodies targeting antigens expressed on tumor cells. Another milestone was achieved in the early 2010s with the approval of antibodies targeting immune checkpoints. Nowadays, the discovery and development of new biological entities and biological therapeutic products represent a rapidly growing market in various therapeutic areas, with about 10 to 15 biologics being approved each year. We have built a premium expert ecosystem services - DRIVE-Biologics - to support and accelerate biologics drug discovery and development in oncology, immuno-oncology, and inflammatory diseases. The DRIVE-Biologics consortium provides a unique integrated solution with specialist services from strategic partners, to access market analysis, establish the ability to design, optimize, and develop novel biological entities addressing the therapeutic target of interest. DRIVE-Biologics supplies the high-level, IND-focused discipline to rigorously manage the integrated programs integrating CMC, manufacturing, regulatory affairs and clinical trial. We will present the lead optimization and multiparameter preclinical evaluation process to select and assess biological candidates for downstream development and clinical studies: -Custom cellular model development for discovery and potency analysis; -In vitro screening, target engagement, and mechanism of action elucidation with cellular models ranging from tumor cell lines, immune cells or primary patient samples; -In vivo efficacy and safety studies using refined and highly characterized syngeneic, xenogeneic, patient-derived xenograft or humanized mouse models up to non-human primates; -DMPK capabilities to develop and validate bioanalytical methods (including GLP-compliancy) such as LBA and qPCR/RT-qPCR and also assess immunogenicity; -Biodistribution and tumor specificity analysis of bioconjugated and radiolabeled biologics. Herein, we provide DRIVE-BIO optimization and preclinical evaluation process to select promising biologics and list the key parameters to be checked. We will present our recent results which highlight the importance to optimize these parameters to improve the efficacy of biologics. Citation Format: Emily Fang, Campbell Bunce, Philip Payne, Rafael Teran, Olivier Duchamp, Caroline Mignard, Francis Bichat, Fabrice Viviani. DRIVE-Biologics: All the steps from discovery to development of novel biological entities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1537.

Abstract 3973: Precision RNAi using synthetic shRNAmir target sites

Abstract Genetic loss-of-function methods including short-hairpin RNAs (shRNAs) and CRISPR are key methods for target gene validation across a variety of different disease areas. While these methods have revolutionized target gene discovery and characterization, both methods suffer from limitations in the context of in vivo target validation due to off-target effects and insufficient knock-down for shRNAs and resistant clones for CRISPR. In this study, we describe a method, artificial RNA interference (ARTi) that overcomes these limitations by fundamentally changing the basic experimental strategy of RNAi-based loss-of-function studies. Instead of newly designing gene-specific shRNA for individual target genes, ARTi utilizes ultra-effective and selective artificial miRNA-based shRNAs that do not match any transcribed gene in the target genome. In addition, these sequences are optimized for ultra-efficient miRNA processing and knockdown of synthetic target genes, and are deeply characterized to not trigger major off-target effects, enabling highly stringent target validation in vivo with unprecedented temporal control, selectivity and potency. We validate the approach by studying the in vitro and in vivo phenotypes of EGFR, KRAS and STAG1, genes relevant to cancer biology. This loss-of-function strategy will enable novel experimental strategies in therapeutic target validation and will be instrumental in guiding the lead optimization process by establishing genetic benchmark phenotypes. Citation Format: Ralph Neumüller, Thomas Hoffmann, Alexandra Hörmann, Maja Corcokovic, Johannes Zuber. Precision RNAi using synthetic shRNAmir target sites. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3973.

Targeting Enterococcus faecalis HMG-CoA reductase with a non-statin inhibitor

HMG-CoA reductase (HMGR), a rate-limiting enzyme of the mevalonate pathway in Gram-positive pathogenic bacteria, is an attractive target for development of novel antibiotics. In this study, we report the crystal structures of HMGR from Enterococcus faecalis (efHMGR) in the apo and liganded forms, highlighting several unique features of this enzyme. Statins, which inhibit the human enzyme with nanomolar affinity, perform poorly against the bacterial HMGR homologs. We also report a potent competitive inhibitor (Chembridge2 ID 7828315 or compound 315) of the efHMGR enzyme identified by a high-throughput, in-vitro screening. The X-ray crystal structure of efHMGR in complex with 315 was determined to 1.27 Å resolution revealing that the inhibitor occupies the mevalonate-binding site and interacts with several key active site residues conserved among bacterial homologs. Importantly, 315 does not inhibit the human HMGR. Our identification of a selective, non-statin inhibitor of bacterial HMG-CoA reductases will be instrumental in lead optimization and development of novel antibacterial drug candidates.

Automated quality control tool for high-content imaging data by building 2D prediction intervals on reference biosignatures.

Recent advances in automated microscopy and image analysis enables quantitative profiling of cellular phenotypes (Cell Painting). It paves the way for studying the broad effects of chemical perturbations on biological systems at large scale during lead optimization. Comparison of perturbation biosignatures with biosignatures of annotated compounds can inform on both on- and off-target effects. When building databases with phenotypic profiles of thousands of compounds, it is vital to control the quality of Cell Painting assays over time. A tool for this to our knowledge does not yet exist within the imaging community. In this paper, we introduce an automated tool to assess the quality of Cell Painting assays by quantifying the reproducibility of biosignatures of annotated reference compounds. The tool learns the biosignature of those treatments from a historical dataset, and subsequently, it builds a two-dimensional probabilistic quality control (QC) limit. The limit will then be used to detect aberrations in new Cell Painting experiments. The tool is illustrated using simulated data and further demonstrated on Cell Painting data of the A549 cell line. In general, the tool provides a sensitive, detailed and easy-to-interpret mechanism to validate the quality of Cell Painting assays.

Synthesis, Molecular Docking and Characterization of Pyrazole N-Mannich Base Derivatives as Antimicrobial Agents

In this work we aimed to design synthesis and evaluate the N-Mannich bases of pyrazole. A novel series of N-Mannich bases of pyrazole analogues were designed and synthesized with an effort to overcome the increasing antibiotic resistance. Tyrosyl-tRNA synthetase (TyrRS) comprises an N-terminal domain, which has the fold of the class I aminoacyl-tRNA synthetases. Computational Autodock 4.2 tools will be employed in this study for docking of pyrazole ligand molecules against Tyrosyl-tRNA synthetase (TyrRS) of Escherichia coli (PDB code: 1x8x) and Staphylococcus aureus (PDB code: 1jil.pdb). Molinspiration server was used for lead optimization. The ligand molecules were subjected to molecular docking studies with enzyme Tyrosyl-tRNA synthetase. The molecular docking studies are supported to compare in-vitro antibacterial activity by the use of binding energy of the docked ligand molecules. The newly synthesized compounds were characterized by UV, IR and various physico-chemical methods. Further, the antibacterial activity of N-Mannich bases of pyrazole compounds were assessed with zone of inhibition by agar well diffusion method using gram negative bacterial strain Escherichia coli and gram-positive strain staphylococcus aureus. These same compounds were subjected to find the antifungal activity against Aspergillus fumigates and Aspergillus Niger.

Rational Design and Lead Optimisation of Potent Antimalarial Quinazolinediones and Their Cytotoxicity against MCF-7.

Quinazolinedione is one of the most outstanding heterocycles in medicinal chemistry thanks to its wide ranges of biological activities including antimalarial, anticancer, and anti-inflammatory. TCMDC-125133 containing a quinazolinedione pharmacophore displays promising antimalarial activity and low toxicity, as described in the GlaxoSmithKline (GSK) report. Herein, the design and synthesis of novel quinazolinedione derivatives is described on the basis of our previous work on the synthesis of TCMDC-125133, where low-cost chemicals and greener alternatives were used when possible. The initial SAR study focused on the replacement of the valine linker moiety; according to the in silico prediction using SwissADME, concise four-step syntheses toward compounds 4-10 were developed. The in-house synthesized compounds 4-10 were assayed for antimalarial activity against P. falciparum 3D7, and the result revealed that only the compound 2 containing a valine linker was tolerated. Another round of lead optimization focused on the replacement of the m-anisidine moiety in compound 2. A library of 12 derivatives was prepared, and the antimalarial assay showed that potent antimalarial activity could be maintained by replacing the methoxy group in the meta position of the phenyl side chain with a fluorine or chlorine atom (21: IC50 = 36 ± 5 nM, 24: IC50 = 22 ± 5 nM). Further lead optimization is underway to enhance the antimalarial activity of this class of compound. The compounds included in the study possess little to no antiproliferative activity against MCF-7 cells.

Panoramic Review on Progress and Development of Molecular Docking

In structural molecular biology and computer-assisted drug creation, molecular docking is a crucial tool. Predicting the prevailing binding mode (s) of a ligand with a protein having a known three-dimensional structure is the aim of ligand-protein docking. Effective docking methods use a scoring system that correctly ranks candidate dockings and efficiently explore high-dimensional spaces. Lead optimization benefits greatly from the use of docking to do virtual screening on huge libraries of compounds, rate the outcomes, and offer structural ideas for how the ligands inhibit the target. It can be difficult to interpret the findings of stochastic search methods, and setting up the input structures for docking is just as crucial as docking itself. In recent years, computer-assisted drug design has relied heavily on the molecular docking technique to estimate the binding affinity and assess the interactive mode since it can significantly increase efficiency and lower research costs. The main concepts, techniques, and frequently utilized molecular docking applications are introduced in this work. Additionally, it contrasts the most popular docking applications and suggests relevant study fields. Finally, a brief summary of recent developments in molecular docking, including the integrated technique and deep learning, is provided. Current docking applications are not precise enough to forecast the binding affinity due to the insufficient molecular structure and the inadequacies of the scoring mechanism.

Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility.

Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking programs for pose prediction are optimized for redocking to an existing cocrystallized protein structure, ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Flexible protein-ligand docking still remains a significant challenge to computational drug design. To target this challenge, we present a deep learning (DL) model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of iterative search algorithms obsolete. The model was trained on a large-scale data set of protein-ligand complexes and evaluated on independent test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.

To Design Scalable Free Energy Perturbation Networks, Optimal Is Not Enough.

Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the simulation experiment, using graphs where nodes represent ligands and graph edges represent alchemical transformations between ligands. Recent work demonstrated that optimizing the statistical architecture of these perturbation graphs improves the accuracy of the predicted changes in the free energy of ligand binding. Therefore, to improve the success rate of computational drug discovery, we present the open-source software package High Information Mapper (HiMap)─a new take on its predecessor, Lead Optimization Mapper (LOMAP). HiMap removes heuristics decisions from design selection and instead finds statistically optimal graphs over ligands clustered with machine learning. Beyond optimal design generation, we present theoretical insights for designing alchemical perturbation maps. Some of these results include that for n number of nodes, the precision of perturbation maps is stable at n·ln(n) edges. This result indicates that even an "optimal" graph can result in unexpectedly high errors if a plan includes too few alchemical transformations for the given number of ligands and edges. And, as a study compares more ligands, the performance of even optimal graphs will deteriorate with linear scaling of the edge count. In this sense, ensuring an A- or D-optimal topology is not enough to produce robust errors. We additionally find that optimal designs will converge more rapidly than radial and LOMAP designs. Moreover, we derive bounds for how clustering reduces cost for designs with a constant expected relative error per cluster, invariant of the size of the design. These results inform how to best design perturbation maps for computational drug discovery and have broader implications for experimental design.

Negishi Cross‐Coupling in the Preparation of Benzyl Substituted Pyrrolo[2,3‐d]pyrimidine Based CSF1R Inhibitors

AbstractThe colony‐stimulating factor 1 receptor (CSF1R) is a protein kinase emerging as an attractive target with clinical relevance in cancer, CNS and inflammatory diseases. Molecular docking experiments followed by synthesis and structure–activity relationship have been used to identify low molecular weight structures as promising hits for lead optimization. These molecules are synthesized from a 4‐chloro‐6‐iodo‐pyrrolo[2,3‐d]pyrimidine building block using Negishi and Suzuki–Miyaura cross‐coupling reactions in high yields. Several inhibitors possessed excellent enzymatic potency, and the parent compound preferably binds to the autoinhibited form of CSF1R. Cellular and in vivo profiling indicate that further tuning of drug structure is needed prior to efficacy studies.

Advantages of X-ray Crystallography in Drug Discovery Research

The paradigm shift in drug discovery that began in the early 1990s was the development of Structure Based Drug Design（SBDD）as a means of rational drug design for disease targets. The greatest advantage of X-ray analysis is that once the crystal structure is obtained, structural information can be fed back very quickly under almost the same conditions for soaking and co-crystallization, contributing to lead optimization in the early stages of drug discovery. In addition, it is unique in that it covers target molecules from small molecular weights（20-30 kDa）to large molecular weights（100 kDa～）. On the other hand, it has many weak points such as dynamic structure close to the physiological state, identification of hydrogen atoms, and acquisition of charge information, which, when combined with multiple methods in a complementary manner, will contribute to a significant reduction in the lead optimization period in the future.

An improved 3D quantitative structure-activity relationships (QSAR) of molecules with CNN-based partial least squares model

Ligand-based virtual screening plays an important role for cases in which protein structures are not available. Among ligand-based methods, accurate and fast prediction of protein-ligand binding affinity is crucial for reducing computational cost and exploring the chemical search space efficiently. Here we proposed a CNN-based method, termed as L3D-PLS for building the quantitative structure-activity relationships without target structures. In L3D-PLS, a CNN module was designed for extracting the key interaction features from the grids around aligned ligands, and a partial least square (PLS) model fits the binding affinity with the extracted features of the pre-trained CNN module. In 30 publicly available pre-aligned molecular datasets, L3D-PLS outperformed the traditional CoMFA method. This results highlight that L3D-PLS can be useful for lead optimization based on small datasets which is often true in drug discovery compaign.

Fast-Killing Tyrosine Amide ((S)-SW228703) with Blood- and Liver-Stage Antimalarial Activity Associated with the Cyclic Amine Resistance Locus (PfCARL).

Current malaria treatments are threatened by drug resistance, and new drugs are urgently needed. In a phenotypic screen for new antimalarials, we identified (S)-SW228703 ((S)-SW703), a tyrosine amide with asexual blood and liver stage activity and a fast-killing profile. Resistance to (S)-SW703 is associated with mutations in the Plasmodium falciparum cyclic amine resistance locus (PfCARL) and P. falciparum acetyl CoA transporter (PfACT), similarly to several other compounds that share features such as fast activity and liver-stage activity. Compounds with these resistance mechanisms are thought to act in the ER, though their targets are unknown. The tyramine of (S)-SW703 is shared with some reported PfCARL-associated compounds; however, we observed that strict S-stereochemistry was required for the activity of (S)-SW703, suggesting differences in the mechanism of action or binding mode. (S)-SW703 provides a new chemical series with broad activity for multiple life-cycle stages and a fast-killing mechanism of action, available for lead optimization to generate new treatments for malaria.

Evaluation of bottom-up modeling of the blood-brain barrier to improve brain penetration prediction via physiologically based pharmacokinetic modeling.

Predicting the brain penetration of drugs has been notoriously difficult; however, recently, permeability-limited brain models have been constructed. Lead optimization for central nervous system compounds often focuses on compounds that have low transporter efflux, where passive permeability could be a main driver in determining cerebrospinal fluid (CSF)/brain concentrations. The main objective of this study was to evaluate the translatability of passive permeability data generated from different in vitro systems and its impact on the prediction of human CSF/brain concentrations using physiologically-based pharmacokinetic (PBPK) modeling. In vitro data were generated using gMDCK and parallel artificial membrane permeability assay-blood-brain barrier for comparison and predictions using a quantitative structure-activity relationship model were also evaluated. PBPK modeling was then performed for seven compounds with moderate-high permeability and a range of efflux in vitro, and the CSF/brain mass concentrations and Kpuu were reasonably predicted. This work provides the first step of a promising approach using bottom-up PBPK modeling for CSF/brain penetration prediction to support lead optimization and clinical candidate selection.