Hit Finding Research Articles

CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson's Disease Associated Protein.

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson's disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a KD lower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

Data mining of PubChem bioassay records reveals diverse OXPHOS inhibitory chemotypes as potential therapeutic agents against ovarian cancer

Focused screening on target-prioritized compound sets can be an efficient alternative to high throughput screening (HTS). For most biomolecular targets, compound prioritization models depend on prior screening data or a target structure. For phenotypic or multi-protein pathway targets, it may not be clear which public assay records provide relevant data. The question also arises as to whether data collected from disparate assays might be usefully consolidated. Here, we report on the development and application of a data mining pipeline to examine these issues. To illustrate, we focus on identifying inhibitors of oxidative phosphorylation, a druggable metabolic process in epithelial ovarian tumors. The pipeline compiled 8415 available OXPHOS-related bioassays in the PubChem data repository involving 312,093 unique compound records. Application of PubChem assay activity annotations, PAINS (Pan Assay Interference Compounds), and Lipinski-like bioavailability filters yields 1852 putative OXPHOS-active compounds that fall into 464 clusters. These chemotypes are diverse but have relatively high hydrophobicity and molecular weight but lower complexity and drug-likeness. These chemotypes show a high abundance of bicyclic ring systems and oxygen containing functional groups including ketones, allylic oxides (alpha/beta unsaturated carbonyls), hydroxyl groups, and ethers. In contrast, amide and primary amine functional groups have a notably lower than random prevalence. UMAP representation of the chemical space shows strong divergence in the regions occupied by OXPHOS-inactive and -active compounds. Of the six compounds selected for biological testing, 4 showed statistically significant inhibition of electron transport in bioenergetics assays. Two of these four compounds, lacidipine and esbiothrin, increased in intracellular oxygen radicals (a major hallmark of most OXPHOS inhibitors) and decreased the viability of two ovarian cancer cell lines, ID8 and OVCAR5. Finally, data from the pipeline were used to train random forest and support vector classifiers that effectively prioritized OXPHOS inhibitory compounds within a held-out test set (ROCAUC 0.962 and 0.927, respectively) and on another set containing 44 documented OXPHOS inhibitors outside of the training set (ROCAUC 0.900 and 0.823). This prototype pipeline is extensible and could be adapted for focus screening on other phenotypic targets for which sufficient public data are available.Scientific contributionHere, we describe and apply an assay data mining pipeline to compile, process, filter, and mine public bioassay data. We believe the procedure may be more broadly applied to guide compound selection in early-stage hit finding on novel multi-protein mechanistic or phenotypic targets. To demonstrate the utility of our approach, we apply a data mining strategy on a large set of public assay data to find drug-like molecules that inhibit oxidative phosphorylation (OXPHOS) as candidates for ovarian cancer therapies.Graphical

The freedom space - a new set of commercially available molecules for hit discovery.

The advent of high-performance virtual screening techniques nowadays allows drug designers to explore ultra-large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug-likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless "hits", by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery. Here we describe a new dataset of make-on-demand compounds called the Freedom space. It follows the principles of Enamine REAL Space and contains highly feasible molecules (synthesis success rate over 75 percent). However, the scaffold and chemography analysis revealed significant differences to both the REAL and biologically annotated compounds from the ChEMBL database. The Freedom Space is a significant extension of the REAL Space and can be utilized for a more comprehensive exploration of the synthetically feasible chemical space in hit finding and hit-to-lead campaigns.

Highly Selective Novel Heme Oxygenase-1 Hits Found by DNA-Encoded Library Machine Learning beyond the DEL Chemical Space.

DNA-encoded library (DEL) technology, especially when combined with machine learning (ML), is a powerful method to discover novel inhibitors. DEL-ML can expand a larger chemical space and boost cost-effectiveness during hit finding. Heme oxygenase-1 (HO-1), a heme-degrading enzyme, is linked to diseases such as cancer and neurodegenerative disorders. The discovery of five series of new scaffold HO-1 hits is reported here, using a DEL-ML workflow, which emphasizes the model's uncertainty quantification and domain of applicability. This model exhibits a strong extrapolation ability, identifying new structures beyond the DEL chemical space. About 37% of predicted molecules showed a binding affinity of K D < 20 μM, with the strongest being 141 nM, amd 14 of those molecules displayed >100-fold selectivity for HO-1 over heme oxygenase-2 (HO-2). These molecules also showed structural novelty compared to existing HO-1 inhibitors. Docking simulations provided insights into possible selectivity rationale.

Chasing Red Herrings: Palladium Metal Salt Impurities Feigning KRAS Activity in Biochemical Assays.

Identifying promising chemical starting points for small molecule inhibitors of active, GTP-loaded KRAS "on" remains of great importance to clinical oncology and represents a significant challenge in medicinal chemistry. Here, we describe broadly applicable learnings from a KRAS hit finding campaign: While we initially identified KRAS inhibitors in a biochemical high-throughput screen, we later discovered that compound potencies were all but assay artifacts linked to metal salts interfering with KRAS AlphaScreen assay technology. The source of the apparent biochemical KRAS inhibition was ultimately traced to unavoidable palladium impurities from chemical synthesis. This discovery led to the development of a Metal Ion Interference Set (MIIS) for up-front assay development and testing. Profiling of the MIIS across 74 assays revealed a reduced interference liability of label-free biophysical assays and, as a result, provided general estimates for luminescence- and fluorescence-based assay susceptibility to metal salt interference.

Abstract A003: Unwinding the complexities of helicases as compelling drug targets in oncology

Abstract In recent years, helicases have moved to the forefront of research as novel drug targets for a variety of human diseases, including cancer. As critical components of the cell cycle and DNA repair, these enzymes offer an attractive point of therapeutic intervention. Recent bioinformatic endeavours such as the Cancer DepMap have further highlighted the essentiality of these proteins in the progression of numerous cancers. Inhibitors of Werner helicase (WRN) entered Phase I evaluation in 2023, after studies highlighted its role in tumours with microsatellite instability (MSI) and impaired mismatch repair (MMR). Building on these advances, helicases now represent a significant drug target class and are the subject of intense research. However, these targets present significant complexities in their prosecution. Hit finding and robust validation of these putative active compounds remains challenging, with high attrition rates and significant sensitivity to false positives. Resultant hits require thorough biological characterisation via both biochemical and biophysical methods, alongside determination of selectivity against other closely related family members, to increase confidence in their provenance before commencing a detailed drug discovery project. To exemplify these approaches, we have profiled three commercially available WRN inhibitors from two chemical series through a selection of biochemical and biophysical assays. The three compounds exhibit excellent selectivity against 6 distinct helicase/translocase targets. In Michaelis-Menten mechanism of Inhibition studies, we defined contrasting mechanisms of inhibition for the two series and were also able to identify time dependent inhibition with one compound series. Analysing binding interactions of the compounds against protein and protein:substrate complexes, via a fluorescent thermal shift assay (FTSA) and microscale thermophoresis (MST), enabled us to further understand and interrogate the mechanism of inhibition. Herein, we describe some of our experiences, learnings and best practices when prosecuting these compelling, but challenging, targets. Citation Format: Yael Mamane, Zoe Caple, Katie Chapman, Ian Henderson, Allan Jordan, Vanessa Lyne, Cynthia Okoye, Laurent Rigoreau, Ganesh Kadamur, Stuart Thomson, Chris Tomlinson, Jana Wolf. Unwinding the complexities of helicases as compelling drug targets in oncology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A003.

Innovative On-DNA Synthesis of Sulfides and Sulfoximines: Enriching the DEL Synthesis Toolbox.

DNA-encoded library (DEL) technologies enable the fast exploration of gigantic chemical space to identify ligands for the target protein of interest and have become a powerful hit finding tool for drug discovery projects. However, amenable DEL chemistry is restricted to a handful of reactions, limiting the creativity of drug hunters. Here, we describe a new on-DNA synthetic pathway to access sulfides and sulfoximines. These moieties, usually contemplated as challenging to achieve through alkylation and oxidation, can now be leveraged in routine DEL selection campaigns.

POLARISED views and FRETting about probe modulation assays: Learning from High Throughput Screening

Fluorescent probe modulation assays are a widely used approach to monitor displacement or stabilisation of fluorescently labelled tool ligands by test compounds. These assays allow an optical read-out of probe-receptor binding and can be used to detect compounds that compete with the labelled ligand, either directly or indirectly. Probes for both orthosteric and allosteric sites are often employed. The method can also be used to identify test compounds that may stabilise the ternary complex, offering an opportunity to discover novel molecular glues. The utility of these fluorescence-based assays within high-throughput screening has been facilitated by the use of streptavidin labelled terbium as a donor and access to a range of different acceptor fluorophores. During 2023, the High-throughput Screening group at AstraZeneca carried out 8 high-throughput screens using these approaches. In this manuscript we will present the types of assays used, an overview of the timelines for assay development and screening, the application of orthogonal artefact methods to aid hit finding and the results of the screens in terms of hit rate and the number of compounds identified with IC50 values of better than 30 µM. Learning across the development, execution and analysis of these screens will be presented.

DNA-Encoded Library Technology─A Catalyst for Covalent Ligand Discovery.

The identification of novel covalent ligands for therapeutic purposes has long depended on serendipity, with dedicated hit finding techniques emerging only in the early 2000s. Advances in chemoproteomics have enabled robust characterization of putative drugs to derisk the unique liabilities associated with covalent hit molecules, leading to a renewed interest in this targeting modality. DNA-encoded library (DEL) technology has similarly emerged over the past two decades as a highly efficient method to identify new chemical equity toward protein targets of interest. A number of commercial and academic groups have reported methods in covalent DEL synthesis and hit identification; however, it is evident that there is still much to be done to fully realize the power of this technology for covalent ligand discovery. This perspective will explore the current approaches in covalent DEL technology and reflect on the next steps to advance this field.

Abstract 3107: SMASH: Single molecule antibody screening with high-throughput imaging system

Abstract Larger, more diverse antibody libraries are being screened in recent years. Despite enabling the high-throughput screening of extensive libraries, the process for identification and characterization of antibodies remains laborious and time-intensive. Here we report a platform that not only compiles hit finding and validation processes, but also provides high-resolution binding affinity as well as rank order among the library being screened. We streamlined the library construction and affinity screening via microscale transient expression in HEK293T cells with designed antibody sequence and direct quantification of antibody-antigen binding affinity which reduced the timeline from multiple weeks to a single week. We constructed the single-chain fragment antigen binding (scFab) libraries by replacing the duplex DNA fragment in CDR of interest while preserving the human framework of Fab. We have demonstrated that the SPID technique allows to detect antibody-antigen interactions from just dozens of pg of scFabs in total. This allowed us to bypass the conventional DNA cloning or even PCR amplification to achieve the minimum amount of DNA plasmids for expressing scFabs to be analyzed such as BLI and SPR. Therefore, we assembled the DNA plasmids through in vitro ligation with high efficiency and directly introduced it to HEK293T in 96-well microplate with microscale (200 ul) for transient transfection, which only takes three days for library generation. Next, we utilized SPID to quantify the number of single molecules between free and antigen-bound fractions of scFab, resulting in occupancy at the antibody-antigen binding equilibrium. This technique facilitated the determination of binding affinity (KD) for 200 antibody-antigen pairs within a 6-hour TAT encompassing both measurement and analysis. In total, we utilized 4,000 times less antibody for characterization while achieving enhanced sensitivity compared to both BLI and SPR methods. As a proof-of-concept, we generated a library consisting of 720 variants of Trastuzumab in scFab format to probe the landscape of CDRs affecting affinities against HER2 showing nM to low uM dynamic range of KD. Out of 720 analyzed variants, 5% were deleterious, 38% provided substantial changes, 40% showed little/no change, 17% enhanced affinity to HER2. To validate the robustness of the platform, we extensively expanded the library screening, assessing over 2,000 antibody variants for their affinity against more than three different therapeutic targets. To conclude, we demonstrated an efficient approach for monitoring changes in affinity resulting from single mutations in scFab. This rapid screening of site-saturated libraries would lead to a combinational variant library that our platform can screen. Ultimately, our platform aims to identify potential candidates for therapeutic antibody development throughput the phases of antibody discovery, maturation, and engineering. Citation Format: Jihye Jo, Changju Chun, Byeong-Kwon Sohn, Booyoung Yu, Jiyu Lee. SMASH: Single molecule antibody screening with high-throughput imaging system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3107.

Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery

We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure–activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.

Challenges for the Discovery of Non-Covalent WRN Helicase Inhibitors.

The Werner Syndrome RecQ helicase (WRN) is a synthetic lethal target of interest for the treatment of cancers with microsatellite instability (MSI). Different hit finding approaches were initially tested. The identification of WRN inhibitors proved challenging due to a high propensity for artefacts via protein interference, i. e., hits inhibiting WRN enzymatic activities through multiple, unspecific mechanisms. Previously published WRN Helicase inhibitors (ML216, NSC19630 or NSC617145) were characterized in an extensive set of biochemical and biophysical assays and could be ruled out as specific WRN helicase probes. More innovative screening strategies need to be developed for successful drug discovery of non-covalent WRN helicase inhibitors.

The Pixel Anomaly Detection Tool: a user-friendly GUI for classifying detector frames using machine-learning approaches.

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model.

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking.

Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.

Rapid PROTAC Discovery Platform: Nanomole-Scale Array Synthesis and Direct Screening of Reaction Mixtures.

Precise length, shape, and linker attachment points are all integral components to designing efficacious proteolysis targeting chimeras (PROTACs). Due to the synthetic complexity of these heterobifunctional degraders and the difficulty of computational modeling to aid PROTAC design, the exploration of structure-activity relationships remains mostly empirical, which requires a significant investment of time and resources. To facilitate rapid hit finding, we developed capabilities for PROTAC parallel synthesis and purification by harnessing an array of preformed E3-ligand-linker intermediates. In the next iteration of this approach, we developed a rapid, nanomole-scale PROTAC synthesis methodology using amide coupling that enables direct screening of nonpurified reaction mixtures in cell-based degradation assays, as well as logD and EPSA measurements. This approach greatly expands and accelerates PROTAC SAR exploration (5 days instead of several weeks) as well as avoids laborious and solvent-demanding purification of the reaction mixtures, thus making it an economical and more sustainable methodology for PROTAC hit finding.

High-throughput differential scanning fluorimetry (DSF) and cellular thermal shift assays (CETSA): Shifting from manual to automated screening

Biophysical affinity screening is increasingly being adopted as a high-throughput hit finding technique in drug discovery. Automation is highly beneficial to high-throughput screening (HTS) since a large number of compounds need to be reproducibly tested against a biological target. Herein, we describe how we have automated two biophysical affinity screening methods that rely on a thermal shift in protein melting temperature upon small molecule binding: differential scanning fluorimetry (DSF) and the cellular thermal shift assay (CETSA).

Towards the development of a chemical probe targeting the disruption of SYK tandem SH2 domain and FCεR1γ interactions for Alzheimer’s disease

AbstractBackgroundSpleen tyrosine kinase (SYK) and FCεR1γ were identified as targets for Alzheimer’s disease (AD) via multi‐omics human data by AMP‐AD teams. These proteins were selected for further target validation and medicinal chemistry efforts by members of the Target Enablement to Accelerate Therapy Development for Alzheimer’s Disease (TREAT‐AD) program. SYK has a reported role in AD (PMID: 31324720), and SYK activation can occur upon binding to the phospho‐ITAM region of the FCεR1γ receptor via the tandem SH2 domains (tSH2) of SYK (PMID: 29440271). Therefore, disruption of this interaction is proposed to slow AD related pathology. Through the generation of target enabling packages (TEPs) we aim to provide experimental validation of these targets for AD and develop a high‐quality chemical probe that targets this interaction.MethodFirstly, tSH2 domains of SYK were purified, followed by crystallography of these tSH2 in the presence of phospho‐ITAM‐containing peptides. A TR‐FRET‐based assay was developed using the SYK tSH2 domain and phospho‐ITAM peptide of FCεR1γ. This assay was miniaturized for ultra‐high‐throughput screening (uHTS) of a screening library of compounds. In parallel, a DNA encoded library (DEL) screen was executed to identify compounds that bind the SYK tSH2 domain. The hits generated using these hit finding campaigns were validated in dose‐response and via orthogonal biophysical methods. Medicinal chemistry campaigns to expand upon these hits are underway.ResultWe have crystallography data of the SYK tSH2 domains bound to phospho‐ITAM peptides and have established a TR‐FRET based assay to identify inhibitors of the interaction between FCεR1γ and SYK. Hit compounds have been identified through uHTS and DEL screening methods. Further validation of these hits in orthogonal assays are underway, alongside medicinal chemistry efforts to identify more potent analogues.ConclusionThrough the generation of a SYK/FCεR1γ TEP, we have provided the tools to allow for experimental validation of these target for AD and have begun to generate inhibitors of this interaction.

Thermal unfolding methods in drug discovery.

Thermal unfolding methods, applied in both isolated protein and cell-based settings, are increasingly used to identify and characterize hits during early drug discovery. Technical developments over recent years have facilitated their application in high-throughput approaches, and they now are used more frequently for primary screening. Widespread access to instrumentation and automation, the ability to miniaturize, as well as the capability and capacity to generate the appropriate scale and quality of protein and cell reagents have all played a part in these advances. As the nature of drug targets and approaches to their modulation have evolved, these methods have broadened our ability to provide useful chemical start points. Target proteins without catalytic function, or those that may be difficult to express and purify, are amenable to these methods. Here, we provide a review of the applications of thermal unfolding methods applied in hit finding during early drug discovery.

Abstract 1722: KRAS, BRAF, BRCA1 and EGFR mutation-specific panels using hydrogel-based 3D in vitro tumor models

Abstract Mutation-targeting therapies have become a transformative technology and in many ways a new paradigm for cancer treatment for patients. The early development of Erlotinib for non-small cell lung cancer paved the way for EGFR treatment, irrespective of tumor indication subtype. And the more recent launch of Sotorasib for KRASG12C mutation has marked a turning point for the once undruggable mutation. The advent of gene specific-treatments requires a reconfiguration of preclinical models which more effectively represent the said mutation in populations of diverse backgrounds and tumor indications. 3D models which have the ability to recreate the key hallmarks of cancer - such as growth, invasion, immune infiltration and suppression, stromal transformation, drug diffusion, as well as gene mutational signatures - are of exceptional interest to drive rapid and scalable compound efficacy screening. Here, we developed four 3D in vitro tumor panels carrying mutations in one of the following genes - KRAS, EGFR, BRCA1, and BRAF - as well as wildtype models for comparison. The tumors were grown in a hydrogel matrix in 96-well plates with stromal fibroblasts and screened with the standard of care (e.g. Sotorasib for KRASG12C, Erlotinib for EGFRmut, the PARP inhibitor Olaparib for BRCAmut, and Vemurafenib for BRAFV600E) and other known therapeutic compounds that target their respective mutations. The utility of a 3D hydrogel matrix and fibroblasts in the panels’ models were key in recreating the stromal compartment of the tumor microenvironment. Endpoints were determined within four days of treatment, including tumor size reduction and tumor killing, via high-content image analysis. The Panels’ robustness as demonstrated by the reproducible results in independent experiments suggests they can be utilized at in vitro pharmacology stages and earlier in Hit and Lead finding screens. Citation Format: Bin Xue, Julia Schüler, Christopher Harrod, Zoji Bomya, Kolin C. Hribar. KRAS, BRAF, BRCA1 and EGFR mutation-specific panels using hydrogel-based 3D in vitro tumor models [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 1722.