Selection Of Drug Candidates Research Articles

Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics.

Development of antibodies often begins with the assessment and optimization of their physicochemical properties, and their efficient engagement with the target of interest. Decisions at the early optimization stage are critical for the success of the drug candidate but are constrained due to the limited knowledge of the antibody and target pharmacology. In the present work, we propose a machine learning-based target pharmacology assessment framework that utilizes minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML) to infer optimal physicochemical properties of antibodies and their targets. We use a mPBPK model previously developed by our group that incorporates a multivariate quantitative relationship between antibodies' physicochemical properties such as molecular weight (MW), size, charge, and in silico + in vitro derived descriptors with their PK properties. In this study, we perform a high-throughput exploration of virtual antibody drug candidates with varying physicochemical properties (binding affinity, charge, etc.), and virtual target candidates with varying characteristics (baseline expression, half-life, etc.) to unravel rules for antibody drug candidate selection that achieve favorable drug-target interaction, which is defined by target occupancy (TO) percentage. We identified that variations in the antibody dose and dosing scheme, target form (soluble or membrane-bound), antibody charge, and site of action had a significant effect on the TO and selection criteria for antibody drug candidates. By unraveling new design rules for antibody drug properties that are dependent on ML-based TO assessment, we deliver a first-in-class ML-based target pharmacology assessment framework toward better understanding of the biology-specific PK and ADME processes of antibody drug candidate proteins and reduce the overall time for drug development.

AI-Driven Innovation in Skin Kinetics for Transdermal Drug Delivery: Overcoming Barriers and Enhancing Precision

Transdermal drug delivery systems (TDDS) offer an alternative to conventional oral and injectable drug administration by bypassing the gastrointestinal tract and liver metabolism, improving bioavailability, and minimizing systemic side effects. However, widespread adoption of TDDS is limited by challenges such as the skin’s permeability barrier, particularly the stratum corneum, and the need for optimized formulations. Factors like skin type, hydration levels, and age further complicate the development of universally effective solutions. Advances in artificial intelligence (AI) address these challenges through predictive modeling and personalized medicine approaches. Machine learning models trained on extensive molecular datasets predict skin permeability and accelerate the selection of suitable drug candidates. AI-driven algorithms optimize formulations, including penetration enhancers and advanced delivery technologies like microneedles and liposomes, while ensuring safety and efficacy. Personalized TDDS design tailors drug delivery to individual patient profiles, enhancing therapeutic precision. Innovative systems, such as sensor-integrated patches, dynamically adjust drug release based on real-time feedback, ensuring optimal outcomes. AI also streamlines the pharmaceutical process, from disease diagnosis to the prediction of drug distribution in skin layers, enabling efficient formulation development. This review highlights AI’s transformative role in TDDS, including applications of models such as Deep Neural Networks (DNN), Artificial Neural Networks (ANN), BioSIM, COMSOL, K-Nearest Neighbors (KNN), and Set Covering Machine (SVM). These technologies revolutionize TDDS for both skin and non-skin diseases, demonstrating AI’s potential to overcome existing barriers and improve patient care through innovative drug delivery solutions.

Integrating Model-Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation.

The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation. The union of MIDD with AI enables pharmaceutical researchers to optimize drug candidate selection, dosage regimens, and treatment strategies through virtual trials to help derisk drug candidates. However, several challenges, including the availability of relevant, labeled, high-quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. Moreover, regulatory agencies have recognized the need to adapt their guidelines to evaluate recommendations from AI-enhanced MIDD methods. In conclusion, integrating model-driven drug development with AI offers a transformative paradigm for pharmaceutical innovation. By integrating the predictive power of computational models and the data-driven insights of AI, the synergy between these approaches has the potential to accelerate drug discovery, optimize treatment strategies, and usher in a new era of personalized medicine, benefiting patients, researchers, and the pharmaceutical industry as a whole.

Abstract IA008: Leveraging conformational dynamics for improved selectivity

Abstract Maximizing the clinical activity and combinability of oncogene inhibitors requires the development of molecules that can achieve necessary levels of target inhibition at a tolerated and feasible human dose. This is enabled by improving selectivity against off-targets and/or for the mutant form of the target over wild-type. While conventional structural insights can inform the design of selective drug candidates, there are often cases where clear selectivity hypotheses are not readily identifiable. Here, we present two examples illustrating how conformational dynamics can be leveraged to obtain improved selectivity for validated oncogene targets. The first example focuses on the selective inhibition of FGFR2, a well-established target in various cancers harboring FGFR2 fusions or rearrangements. Although clinical efficacy of pan-FGFR inhibitors has been demonstrated, their benefit is limited by FGFR1- and FGFR4- mediated toxicities. We leveraged differences in conformational dynamics between FGFR2 and other FGFRs observed through molecular dynamics simulations to enable the development of Lirafugratinib, the first FGFR2 selective inhibitor. Lirafugratinib inhibits FGFR2 with a high degree of selectivity in pre-clinical models. In cancer patients, Lirafugratinib achieves >95% FGFR2 occupancy at a dose of 70mg once daily, with minimal evidence of inhibition of other FGFR isoforms. This improved selectivity translates to higher objective response rates compared to pan-FGFR inhibitors across multiple tumor types harboring FGFR2 fusions or rearrangements. The second example illustrates mutant-selective targeting of PI3Kα, the most frequently mutated kinase in cancer. While non-mutant selective inhibitors have shown clinical efficacy, their benefit is limited by hyperglycemia caused by inhibition of wild-type PI3Kα. Conformational differences between mutant and wild-type PI3Kα were identified using structural insights combined with molecular dynamics simulations, leading to the discovery of RLY-2608- the first mutant-selective inhibitor of PI3Kα. RLY-2608 binds to a novel allosteric pocket and demonstrates mutant selective inhibition in pre-clinical models. RLY-2608 has a favorable pharmacokinetic profile in cancer patients, with dose-dependent increases in exposure and low peak to trough fluctuations, resulting in high levels of target coverage throughout the dosing interval. When combined with fulvestrant, this translates into a higher objective response rate and lower rate of hyperglycemia in patients with hormone-receptor positive, PI3Kα-mutant breast cancer compared to non-mutant selective PI3Kα inhibitors. These examples demonstrate the power of leveraging conformational dynamics to obtain selective target inhibition, ultimately translating into improved patient outcomes. Citation Format: James Watters. Leveraging conformational dynamics for improved selectivity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr IA008.

Druggability of Pharmaceutical Compounds Using Lipinski Rules with Machine Learning

In the field of pharmaceutical research, identifying promising pharmaceutical compounds is a critical challenge. The observance of Lipinski's Rule of Five (RO5) is a fundamental criterion, but evaluating many compounds manually requires significant resources and time. However, the integration of computational techniques in drug discovery in its early stages has significantly transformed the pharmaceutical industry, enabling further efficient screening and selection of possible drug candidates. Therefore, this study explores RO5 using algorithms of Machine Learning (ML), offering a comprehensive method to predict the druggability of pharmaceutical compounds. The study developed, evaluated, and validated the performance metrics of multiple supervised machine learning models. The best model was used to build an application that can predict and classify potential drug candidates. The findings revealed promising capabilities across all models for drug classification. Among all the explored models, Random Forest (RF), Extreme Gradient Boost (XGBoost), and Decision Tree (DT) classifiers demonstrated exceptional performance, achieving near-perfect accuracy of 99.94%, 99.81% and 99.87% respectively. This highlights the robustness of ensemble learning methods in classifying compounds based on RO5 adherence. The comparative analysis of these models underscores the importance of considering balanced accuracy, precision, F1-score, recall, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) score, interpretability, and computational efficiency when choosing between ML algorithms in drug discovery. The DrugCheckMaster application was subsequently developed using the most predictive model and is now available on Render (https://capstone-project-dc7w.onrender.com/).

Structure-Tissue Exposure/Selectivity Relationship (STR) on Carbamates of Cannabidiol.

The structure-tissue exposure/selectivity relationship (STR) aids in lead optimization to improve drug candidate selection and balance clinical dose, efficacy, and toxicity. In this work, butyrocholinesterase (BuChE)-targeted cannabidiol (CBD) carbamates were used to study the STR in correlation with observed efficacy/toxicity. CBD carbamates with similar structures and same molecular target showed similar/different pharmacokinetics. L2 and L4 had almost same plasma exposure, which was not correlated with their exposure in the brain, while tissue exposure/selectivity was correlated with efficacy/safety. Structural modifications of CBD carbamates not only changed drug plasma exposure, but also altered drug tissue exposure/selectivity. The secondary amine of carbamate can be metabolized into CBD, while the tertiary amine is more stable. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters can be used to predict STR. Therefore, STR can alter drug tissue exposure/selectivity in normal tissues, impacting efficacy/toxicity. The drug optimization process should balance the structure-activity relationship (SAR) and STR of drug candidates for improving clinical trials.

Single-cell technology for drug discovery and development

The success rate of drug development today remains low, with long development cycles and high costs, especially in areas such as oncology, neurology, immunology, and infectious diseases. Single-cell omics, encompassing transcriptomics, genomics, epigenomics, proteomics, and metabolomics enable the analysis of gene expression profiles and cellular heterogeneity from the perspective of individual cells, offering a high-resolution view of their functional diversity. These technologies can help reveal disease mechanisms, drug target identification and validation, selection of preclinical models and candidate drugs, and clinical decision-making based on disease response to drugs, all at the single-cell level. The development of deep learning technology has provided a powerful tool for research in drug discovery based on single-cell techniques, which has evolved with the advent of large-scale public databases to predict drug responses and targets. In addition, traditional Chinese medicine (TCMs) research has also entered the era of single-cell technology. Single-cell omics technologies offer an alternative way in deciphering the mechanisms of TCMs in disease treatment, revealing drug targets, screening new drugs, and designing combinations of TCMs. This review aims to explore the application of single-cell omics technologies in drug screening and development comprehensively, highlighting how they accelerate the drug development process and facilitate personalized medicine by precisely identifying therapeutic targets, predicting drug responsiveness, deciphering mechanisms of action. It is also concluded that drug development process and therapeutic efficacy of drugs can be improved by combining single-cell omics and artificial intelligence techniques.

Drug Repurposing for Effective Alzheimer's Disease Medicines: Existing Methods and Novel Pharmacoepidemiological Approaches.

Drug repurposing is a methodology used to identify new clinical indications for existing drugs developed for other indications and has been successfully applied in the treatment of numerous conditions. Alzheimer's disease (AD) may be particularly well-suited to the application of drug repurposing methods given the absence of effective therapies and abundance of multi-omic data that has been generated in AD patients recently that may facilitate discovery of candidate AD drugs. A recent focus of drug repurposing has been in the application of pharmacoepidemiologic approaches to drug evaluation. Here, real-world clinical datasets with large numbers of patients are leveraged to establish observational efficacy of candidate drugs for further evaluation in disease models and clinical trials. In this review, we provide a selected overview of methods for drug repurposing, including signature matching, network analysis, molecular docking, phenotypic screening, semantic network, and pharmacoepidemiological analyses. Numerous methods have also been applied specifically to AD with the aim of nominating novel drug candidates for evaluation. These approaches, however, are prone to numerous limitations and potential biases that we have sought to address in the Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study, a multi-step framework for selection and validation of potential drug candidates that has demonstrated the promise of STAT3 inhibitors and re-evaluated evidence for other drug candidates, such as phosphodiesterase inhibitors. Taken together, drug repurposing holds significant promise for development of novel AD therapeutics, particularly as the pace of data generation and development of analytical methods continue to accelerate.

Dimeric natural product panepocyclinol A inhibits STAT3 via di-covalent modification

Homo- or heterodimeric compounds that affect dimeric protein function through interaction between monomeric moieties and protein subunits can serve as valuable sources of potent and selective drug candidates. Here, we screened an in-house dimeric natural product collection, and panepocyclinol A (PecA) emerged as a selective and potent STAT3 inhibitor with profound anti-tumor efficacy. Through cross-linking C712/C718 residues in separate STAT3 monomers with two distinct Michael receptors, PecA inhibits STAT3 DNA binding affinity and transcription activity. Molecular dynamics simulation reveals the key conformation changes of STAT3 dimers upon the di-covalent binding with PecA that abolishes its DNA interactions. Furthermore, PecA exhibits high efficacy against anaplastic large T cell lymphoma in vitro and in vivo, especially those with constitutively activated STAT3 or STAT3Y640F. In summary, our study describes a distinct and effective di-covalent modification for the dimeric compound PecA to disrupt STAT3 function.

Design, synthesis, in vitro and in silico studies of novel piperidine derived thiosemicarbazones as inhibitors of dihydrofolate reductase.

Dihydrofolate reductase (DHFR), an essential enzyme in folate metabolism, presents a promising target for drug development against various diseases, including cancer and tuberculosis. Herein, we present an integrated approach combining in vitro biochemical assays with in silico molecular docking analysis to evaluate the inhibitory potential of 4-piperidine-based thiosemicarbazones 5(a-s) against DHFR. In our in vitro study, a novel series of 4-piperidine-based thiosemicarbazones 5(a-s) were assessed for their inhibitory activity against DHFR enzyme. The synthesized compounds 5(a-s) exhibited potent inhibition with IC50 values in the range of 13.70 ± 0.25 µM to 47.30 ± 0.86 µM. Among all the derivatives 5p displayed highest inhibitory activity. Simultaneously, in silico analysis were performed and compared with standard drug (Methotrexate) to predict the binding affinity and interaction pattern of synthesized compounds with DHFR active site. SAR analysis was done to elucidate how structural modifications impact compound's biological activity, guiding the rational design of potent and selective drug candidates for targeted diseases. These findings may provide a comprehensive assessment of 4-piperdine-based thiosemicarbazones as DHFR inhibitors and contribute to the development of novel therapeutics targeting DHFR-associated diseases.

Use of Structural Alerts for Reactive Metabolites in the Application SpotRM.

Reactive metabolite (RM) formation is widely accepted as playing a crucial role in causing idiosyncratic adverse drug reactions (IADRs), where the liver is most affected. An important goal of drug design is to avoid selection of drug candidates giving rise to RMs and therefore risk causing problems later on involving IADRs. The simplest, initial approach is to avoid test structures that have substructures known or strongly suspected to be associated with IADRs. However, as is evident from the many case reports of IADRs, in most cases a clear association with any (bio)chemical mechanism is lacking, which makes it hard to establish any structure-toxicity relationship. Separate studies of RM formation, in vitro and in vivo, have led to likely evidence and to establishing many structural alerts (SAs) that can be used for fast selection/deselection of planned test compounds. As a background to a discussion of the concept, 25 kinase inhibitor drugs with known problems of hepatotoxicity were probed against a set of SAs contained in the application SpotRM. A clear majority of the probed drugs show liabilities as evident by being flagged by more than one of the fairly established types of SAs. At the same time, no clear SAs were found in three drugs, which is discussed in the broader context of usefulness and selection tactics of SAs in drug design.

Understanding predictions of drug profiles using explainable machine learning models

PurposeThe analysis of absorption, distribution, metabolism, and excretion (ADME) molecular properties is of relevance to drug design, as they directly influence the drug’s effectiveness at its target location. This study concerns their prediction, using explainable Machine Learning (ML) models. The aim of the study is to find which molecular features are relevant to the prediction of the different ADME properties and measure their impact on the predictive model.MethodsThe relative relevance of individual features for ADME activity is gauged by estimating feature importance in ML models’ predictions. Feature importance is calculated using feature permutation and the individual impact of features is measured by SHAP additive explanations.ResultsThe study reveals the relevance of specific molecular descriptors for each ADME property and quantifies their impact on the ADME property prediction.ConclusionThe reported research illustrates how explainable ML models can provide detailed insights about the individual contributions of molecular features to the final prediction of an ADME property, as an effort to support experts in the process of drug candidate selection through a better understanding of the impact of molecular features.

Prediction of the binding interactions between rosmarinic acid and cysteinyl leukotriene receptor type 1 by molecular docking and immobilized receptor chromatography.

Drug-protein interaction analysis is still at the center of research efforts to illustrate binding mechanisms and provide valuable information for selecting drug candidates with ideal properties in the early drug discovery stage. We present the prediction of the binding of rosmarinic acid (RA) to cysteinyl leukotriene receptor type1 (CysLTR1) by molecular docking. According to our findings, CysLTR1 is a potential anti-inflammatory target of RA. Under this assumption, we prepared the immobilized CysLTR1 column via a one-step method and characterized the immobilized CysLTR1 by fluorescent and chromatographic analyses. Furthermore, we used the immobilized CysLTR1 column to evaluate the binding interactions between RA and the immobilized receptor. Molecular docking showed that Tyr 249, Phe 174, Thr 280, Pro 177, and Thr 100 are the main sites for RA to interact with CysLTR1. The main forces that drive the findings are hydrogen bonds and hydrophobic interactions. Characterization results show that CysLTR1 is successfully immobilized with high specificity and stability. Almost no non-specific binding is observed on the immobilized CysLTR1 gels. The association constant and the binding sites are calculated to be 7.268 × 105 L mol-1 and 1.237 × 10-8 mol L-1 by injection amount-dependent method. These results, taken together, confirm the potential target of RA on the anti-inflammatory effect. We believe that it can provide valuable reference information on the in-depth exploration of drug-protein interaction mechanisms, and lead compound screening by this method.

A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Sebetralstat: A Rapidly Acting Oral Plasma Kallikrein Inhibitor for the On-Demand Treatment of Hereditary Angioedema

Sebetralstat is a novel, potent, and selective oral plasma kallikrein inhibitor drug candidate in clinical development for the on-demand treatment of hereditary angioedema (HAE). Upon binding, sebetralstat induces a conformational change in the active site of plasma kallikrein, which contributes to its high potency (Ki 3 nM) and selectivity (>1500 fold) against other serine proteases. Its physiochemical properties promote both rapid dissolution in the stomach and rapid absorption in the upper intestine that contribute to its fast and efficient absorption. A single oral administration of sebetralstat rapidly provides near-complete inhibition of plasma kallikrein and blockade of high-molecular-weight kininogen cleavage as early as 15 min, which drives its clinical efficacy. In a phase 2 clinical trial, sebetralstat significantly reduced the time to beginning of symptom relief (p < 0.0001) with median times of 1.6 h (95% CI: 1.5–3.0) with sebetralstat versus 9.0 h (4.0–17.2) with placebo. KONFIDENT (NCT05259917) is a phase 3 clinical trial assessing the on-demand use of sebetralstat for HAE. If successful, this trial could support the approval of sebetralstat as the first noninvasive, on-demand treatment option to rapidly halt HAE attacks and provide fast symptom relief.

Cryo-EM structures reveal two allosteric inhibition modes of PI3KαH1047R involving a re-shaping of the activation loop

PI3Kα is a lipid kinase that phosphorylates PIP2 and generates PIP3. The hyperactive PI3Kα mutation, H1047R, accounts for about 14% of breast cancer, making it a highly attractive target for drug discovery. Here, we report the cryo-EM structures of PI3KαH1047R bound to two different allosteric inhibitors QR-7909 and QR-8557 at a global resolution of 2.7Å and 3.0Å, respectively. The structures reveal two distinct binding pockets on the opposite sides of the activation loop. Structural and MD simulation analyses show that the allosteric binding of QR-7909 and QR-8557 inhibit PI3KαH1047R hyper-activity by reducing the fluctuation and mobility of the activation loop. Our work provides a strong rational basis for a further optimization and development of highly selective drug candidates to treat PI3KαH1047R-driven cancers.

Abstract 2067: QUANTROseq, a transcriptomic based drug discovery platform using time-resolved gene expression profiling: Identification of inhibitors and degraders of transcription factors and cell signaling regulators

Abstract Integrative analyses of CRISPR/Cas9 screening datasets from different sources revealed - besides known oncogenes - a series of transcriptional master regulators as most prominent context-specific dependencies in cancer that remained largely inaccessible to conventional drug discovery approaches. New chemical entities (NCEs) that specifically interfere with a transcription factor (TF) at any level should induce and be detectable based on selective effects on their primary biochemical activity - i.e., the transcriptional activation of specific target genes. However, the definition of direct targets of a TF and, more generally, the unbiased detection of direct transcriptional effects has remained challenging due to the limited time resolution of conventional techniques for gene perturbation and transcriptional profiling. To overcome this limitation, QUANTRO Therapeutics relies on SLAMseq technology that, by metabolic labeling de novo-synthesized RNA immediately after intervention and conserving the information for later readout, provides unprecedent time-resolution in transcriptomic analysis. Aiming to target previously undruggable targets, we set out to first build definitive knowledge around their direct effect on transcription, inducing their quick protein degradation through the auxin-inducible degron (AID) technology followed by immediate transcriptional readout though SLAMseq -before any secondary, confounding effect could appear in the cell. The targets´ mRNA fingerprints produced are then used as a reference and cross-compared with existing and new chemical entities to: (i) benchmark the real mode of action of existing drugs; (ii) identify new drug candidates that specifically interfere with disease-causing transcriptional programs in cancer; (iii) interrogate the efficiency, selectivity, and potency of drug candidates during Hit-validation and Hit-to-Lead activities. Proof of concept data demonstrated that time-resolved transcriptional profiling can reveal drugs´ mode of action with unprecedent precision and sensitivity, when drugs act on Targets that have a direct effect on transcriptional regulation - such as TFs and signaling pathways. Building on this knowledge, QUANTRO Therapeutics unleashed the unique potential of mRNA fingerprinting technology for HTS-compatible drug discovery by (1) establishing scalable and automated workflows for metabolic RNA labeling, treatment of cells, and RNA extraction; (2) developing algorithms for combinatorial mRNA fingerprint-readout that allow for the simultaneous assessment of TF signatures by multiplexed targeted RNA sequencing (QUANTROseq), thus transforming the precision and scope of cell-based compound screens. Citation Format: Arianna Sabò, Nina Fasching Griesmayr, Tobias Neumann, Raphael Manzenreither, Maja Ivankovic, Rodrigo Pacheco Valamatos Costa, Sarah Rieser, Sara Scinicariello, Petr Triska, Anna Stingeder, Astrid Gruss, Ryan Cubero, Olga Frank, Ivica Sowemimo, Paul Kirchgatterer, Adriana Cantoran, Stefan Ameres, Johannes Zuber, Michael Bauer. QUANTROseq, a transcriptomic based drug discovery platform using time-resolved gene expression profiling: Identification of inhibitors and degraders of transcription factors and cell signaling regulators [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 2067.

Abstract 4876: Data-driven discovery of cancer-specific targets for hepatocellular carcinoma using spatial transcriptomics

Abstract Background: Hepatocellular carcinoma (HCC) is frequently diagnosed in advanced stages, with limited options available for systemic treatment. Given the significant investment of time, the high costs, and the notable failure rates involved in developing new drugs, it is crucial to identify appropriate cancer-specific surface targets, especially aiming at antibody drug conjugates and radioligand therapy. This study aims to propose a pipeline utilizing spatial transcriptomics (ST) to prioritize potential cancer-specific surface targets for HCC. Methods: Visium ST samples were collected from fresh-frozen tissues of HCC patients undergoing hepatectomy. The spots with over 500 expressed genes and a mitochondrial gene percentage below 20% were selected. Counts were normalized with sctransform, and the tumor cell proportion in each spot was predicted using CellDART. Cancer region-specific genes were identified based on three criteria: high topological overlap with tumor cell distribution (using STopover), a high correlation coefficient with tumor cell proportion, or significantly higher expression in the tumor cell cluster compared to others in single-cell RNA-sequencing dataset. Genes identified through these methods were pooled and filtered based on average expression in the whole tissue using human normal cell atlas data and surface proteins. To select drug candidates with low normal organ toxcity, Tabula Sapiens datasets were used. Genes expressed in over 50% of cells and with an average expression above 1 in any critical organs were excluded. Results: One ST sample was obtained from each of the 7 HCC patients. The topology, correlation, and single-cell-based criteria resulted in 42, 15, and 7 genes, respectively, with only 1 gene (P4HB) overlapping across all three methods. After filtering genes with high expression in normal organs based on Tabula Sapiens, 25 genes remained. In the GEPIA databases, GPC3 exhibited the highest log fold change in RNA expression between HCC and normal liver (6.280), followed by TM4SF4 (2.156), EFNA1 (0.581), GJB1 (0.498), and IRS1 (0.391). To externally validate the results, 17 ST datasets from 5 patients provided by HCCDB were used. Among the selected genes, only GPC3 and EFNA1 had higher average expression in the tumor compared to other compartments (normal, stromal, and immune) in at least 3 out of 5 patients. Conclusion: The proposed platform prioritizes cancer-specific surface targets for HCC, focusing primarily on spatial gene expression profiles obtained from ST and gene expression data from external databases. This analytical tool can be further utilized to identify molecular targets for developing antibody drug conjugates or radioligand therapies, which are designed to specifically deliver therapeutic agents to the tumor. Citation Format: Sungwoo Bae, Dongjoo Lee, Daeseung Lee, Kwon Joong Na, Suk Kyun Hong, YoungRok Choi, Nam-Joon Yi, Kwang-Woong Lee, Kyung-Suk Suh, Hongyoon Choi. Data-driven discovery of cancer-specific targets for hepatocellular carcinoma using spatial transcriptomics [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 4876.

The novel drug candidate S2/IAPinh improves survival in models of pancreatic and ovarian cancer

Cancer selective apoptosis remains a therapeutic challenge and off-target toxicity has limited enthusiasm for this target clinically. Sigma-2 ligands (S2) have been shown to enhance the cancer selectivity of small molecule drug candidates by improving internalization. Here, we report the synthesis of a novel drug conjugate, which was created by linking a clinically underperforming SMAC mimetic (second mitochondria-derived activator of caspases; LCL161), an inhibitor (antagonist) of inhibitor of apoptosis proteins (IAPinh) with the sigma-2 ligand SW43, resulting in the new chemical entity S2/IAPinh. Drug potency was assessed via cell viability assays across several pancreatic and ovarian cancer cell lines in comparison with the individual components (S2 and IAPinh) as well as their equimolar mixtures (S2 + IAPinh) both in vitro and in preclinical models of pancreatic and ovarian cancer. Mechanistic studies of S2/IAPinh-mediated cell death were investigated in vitro and in vivo using syngeneic and xenograft mouse models of murine pancreatic and human ovarian cancer, respectively. S2/IAPinh demonstrated markedly improved pharmacological activity in cancer cell lines and primary organoid cultures when compared to the controls. In vivo testing demonstrated a marked reduction in tumor growth rates and increased survival rates when compared to the respective control groups. The predicted mechanism of action of S2/IAPinh was confirmed through assessment of apoptosis pathways and demonstrated strong target degradation (cellular inhibitor of apoptosis proteins-1 [cIAP-1]) and activation of caspases 3 and 8. Taken together, S2/IAPinh demonstrated efficacy in models of pancreatic and ovarian cancer, two challenging malignancies in need of novel treatment concepts. Our data support an in-depth investigation into utilizing S2/IAPinh for the treatment of cancer.

Identification of Protein Target and Selection of Suitable Drug Candidates Against Alzheimer’s Disease by Docking Studies

Identification of Protein Target and Selection of Suitable Drug Candidates Against Alzheimer’s Disease by Docking Studies