Early Drug Discovery Research Articles

Artificial Intelligence: A New Era in Drug Discovery

Artificial intelligence (AI) is a simulation of the process of human intelligence through computers. The process involves obtaining information, developing rules for using information, making possible or accurate conclusions, and self-correcting. The development of new drug residues begins when basic scientists learn about biological targets (receptor, enzyme, protein, and gene). These targets involve the biological processes that occur in patients with a disease. Drug discovery can be through target identification, target verification, lead identification, and effectiveness of lead. AI can offer revolutionary insights into medicine, through data from genetics, proteomics and other life sciences that advance the process of discovery and development. Artificial Intelligence (AI) has recently been developed as a fiery element in the medical care industry. AI has exciting potential for prosperity in the field of biopharmaceutical. The biopharmaceutical industry makes efforts to approach AI to improve drug discovery, reduce research and development costs, reduce the time and cost of early drug discovery, and support predicting potential risks/side effects in late trials that can be very useful in avoiding traumatic events in clinical trials and ultimately clinical trials. Usually, drug development takes five years to go to trial, but the AI drug takes just 12 months. The rapid growth in life sciences and machine learning algorithms has led to enormous statistical access to the growth of AI-based startups focused on drug innovation in recent years.
 

Protein active site prediction for early drug discovery and designing

Abstract Adenosine triphosphate (ATP) is an energy compound present in living organisms and is required by living cells for performing operations such as replication, molecules transportation, chemical synthesis, etc. ATP connects with living cells through specialized sites called ATP-sites. ATP-sites are present in various proteins of a living cell. The life span of a cell can be controlled by controlling ATP compounds and without the provision of energy to ATP compounds, cells cannot survive. Countless diseases treatment (such as cancer, diabetes) can be possible once protein active sites are predicted. Considering the need for an algorithm that predicts ATP-sites with higher accuracy and effectiveness, this research work predicts protein ATP sites in a very novel way. Till now Position-specific scoring matrix (PSSM) along with many physicochemical properties have been used as features with deep neural networks in order to create a model that predicts the ATP-sites. To overcome this problem of complex computation, this exertion proposes k-mer feature vectors with simple machine learning (ML) models to attain the same or even better performance with less computation required. Using 2-mer as feature vectors, this research work trained and tested five different models including KNN, Conv1D, XGBoost, SVM and Random Forest. SVM gave the best performance on k-mer features. The accuracy of the created model is 96%, MCC 90% and ROC-AUC is 99%, which are the same or even better in some aspects than the state-of-the-art results. The state-of-the-art results have an accuracy of 97%, MCC 78% and ROC-AUC is 92%. One of the benefits of the created model is that it is much simpler and more accurate.

The Discovery of Peptide Macrocycle Rescuers of Pathogenic Protein Misfolding and Aggregation by Integrating SICLOPPS Technology and Ultrahigh-Throughput Screening in Bacteria.

The phenomenon of protein misfolding and aggregation has been widely associated with numerous human diseases, such as Alzheimer's disease, systemic amyloidosis and type 2 diabetes, the vast majority of which remain incurable. To advance early stage drug discovery against these diseases, investigation of molecular libraries with expanded diversities and ultrahigh-throughput screening methodologies that allow deeper investigation of chemical space are urgently required. Toward this, we describe how Escherichia coli can be engineered so as to enable (1) the production of expanded combinatorial libraries of short, drug-like, head-to-tail cyclic peptides and (2) their simultaneous functional screening for identifying effective inhibitors of protein misfolding and aggregation using a genetic assay that links protein folding and misfolding to cell fluorescence. In this manner, cyclic peptides with the ability to inhibit pathogenic protein misfolding and/or aggregation can be readily selected by flow cytometric cell sorting in an ultrahigh-throughput fashion. This biotechnological approach accelerates significantly the identification of hit/lead molecules with potentially therapeutic properties against devastating diseases.

A practical guide to large-scale docking.

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Roadmap for the use of base editors to decipher drug mechanism of action.

CRISPR base editors are powerful tools for large-scale mutagenesis studies. This kind of approach can elucidate the mechanism of action of compounds, a key process in drug discovery. Here, we explore the utility of base editors in an early drug discovery context focusing on G-protein coupled receptors. A pooled mutagenesis screening framework was set up based on a modified version of the CRISPR-X base editor system. We determine optimized experimental conditions for mutagenesis where sgRNAs are delivered by cell transfection or viral infection over extended time periods (>14 days), resulting in high mutagenesis produced in a short region located at -4/+8 nucleotides with respect to the sgRNA match. The β2 Adrenergic Receptor (B2AR) was targeted in this way employing a 6xCRE-mCherry reporter system to monitor its response to isoproterenol. The results of our screening indicate that residue 184 of B2AR is crucial for its activation. Based on our experience, we outline the crucial points to consider when designing and performing CRISPR-based pooled mutagenesis screening, including the typical technical hurdles encountered when studying compound pharmacology.

Rapid Identification of Potential Drug Candidates from Multi-Million Compounds' Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening.

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds’ databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.

Advances in TRP channel drug discovery: from target validation to clinical studies.

Transient receptor potential (TRP) channels are multifunctional signalling molecules with many roles in sensory perception and cellular physiology. Therefore, it is not surprising that TRP channels have been implicated in numerous diseases, including hereditary disorders caused by defects in genes encoding TRP channels (TRP channelopathies). Most TRP channels are located at the cell surface, which makes them generally accessible drug targets. Early drug discovery efforts to target TRP channels focused on pain, but as our knowledge of TRP channels and their role in health and disease has grown, these efforts have expanded into new clinical indications, ranging from respiratory disorders through neurological and psychiatric diseases to diabetes and cancer. In this Review, we discuss recent findings in TRP channel structural biology that can affect both drug development and clinical indications. We also discuss the clinical promise of novel TRP channel modulators, aimed at both established and emerging targets. Last, we address the challenges that these compounds may face in clinical practice, including the need for carefully targeted approaches to minimize potential side-effects due to the multifunctional roles of TRP channels.

Development of an intracellular quantitative assay to measure compound binding kinetics

Contemporary drug discovery typically quantifies the effect of a molecule on a biological target using the equilibrium-derived measurements of IC50, EC50, or KD. Kinetic descriptors of drug binding are frequently linked with the effectiveness of a molecule in modulating a disease phenotype; however, these parameters are yet to be fully adopted in early drug discovery. Nanoluciferase bioluminescence resonance energy transfer (NanoBRET) can be used to measure interactions between fluorophore-conjugated probes and luciferase fused target proteins. Here, we describe an intracellular NanoBRET competition assay that can be used to quantify cellular kinetic rates of compound binding to nanoluciferase-fused bromodomain and extra-terminal (BET) proteins. Comparative rates are generated using a cell-free NanoBRET assay and by utilizing orthogonal recombinant protein-based methodologies. A screen of known pan-BET inhibitors is used to demonstrate the value of this approach in the investigation of kinetic selectivity between closely related proteins.

Catalytic Function of Cu (I) and Cu (II) in Total Synthesis of Alkaloids

AbstractRecently, Cu‐catalyzed carbon‐carbon and carbon (sp2)‐nitrogen bond formation have attracted much attention. Nowadays, they are widely being applied to the total synthesis of an extensive assortment of natural products with complex structures. Natural products chemistry is a discrete part of chemical research, which has been significant in the history of chemistry, the sourcing of substances in early preclinical drug discovery research, pharmacology and discovery of folk medicine. Alkaloids are a class of natural product that contains at least one nitrogen atom. They have diverse and important physiological effects on humans and other animals. In fact, as many as one‐quarter of higher plants are estimated to contain alkaloids, of which several thousand (approximately 12000) different types have so far been identified. In this review, we try underscore the total synthesis of naturally occurring alkaloids in which Cu‐promoted reaction is at least one of the vital step. Indeed, in this comprehensive review, the impact of Cu‐catalyzed cross‐coupling reactions in the total synthesis of natural products with a prominence on the evolution of strategies due to copper catalysis will be generally surveyed.

Contact Map Fingerprints of Protein-Ligand Unbinding Trajectories Reveal Mechanisms Determining Residence Times Computed from Scaled Molecular Dynamics.

The binding kinetic properties of potential drugs may significantly influence their subsequent clinical efficacy. Predictions of these properties based on computer simulations provide a useful alternative to their expensive and time-consuming experimental counterparts, even at an early drug discovery stage. Herein, we perform scaled molecular dynamics (ScaledMD) simulations on a set of 27 ligands of HSP90 belonging to more than seven chemical series to estimate their relative residence times. We introduce two new techniques for the analysis and the classification of the simulated unbinding trajectories. The first technique, which helps in estimating the limits of the free energy well around the bound state, and the second one, based on a new contact map fingerprint, allow the description and the comparison of the paths that lead to unbinding. Using these analyses, we find that ScaledMD's relative residence time generally enables the identification of the slowest unbinders. We propose an explanation for the underestimation of the residence times of a subset of compounds, and we investigate how the biasing in ScaledMD can affect the mechanistic insights that can be gained from the simulations.

High Content Image Analysis as a Tool to Morphologically Distinguish Macrophage Activation and Determine Its Importance for Foamy Alveolar Macrophage Responses.

IntroductionLung diseases are an increasing global health burden affecting millions of people worldwide. Only a few new inhaled medicines have reached the market in the last 30 years, in part due to foamy alveolar macrophage (FAM) responses observed in pre-clinical rat studies. The induction mechanism and signaling pathways involved in the development of highly vacuolated ‘foamy’ phenotype is not known. Furthermore, it has not been determined if these observations are adaptive or adverse responses.AimTo determine if high content image analysis techniques can distinguish between alveolar macrophage activation (LPS/IFN-γ activated and IL-4 activated macrophages) and if this could be applied to understanding the generation of ‘foamy’ macrophage phenotypes.MethodsNR8383 rat alveolar macrophages were stimulated with a mix of cytokines (LPS/IFN-γ or IL-4) for 24 h. The cells were further exposed to FAM inducing-compounds amiodarone and staurosporine. Following 24 h incubation, phagocytosis and lipid accumulation were measured using flow cytometry and high content image analysis techniques. The alveolar macrophages responses after exposure to cytokines were assessed by evaluation: (i) cell surface and biochemical markers such as: nitric oxide production, arginase-1 activity and MRC-1 receptor expression (ii) cellular morphology (iii) cellular functionality (phagocytic activity and lipids accumulation).ResultsMacrophages activated with LPS/IFN-γ showed distinct morphological (increased vacuolation) features and functionality (increased lipidosis, decreased phagocytic activity). Foamy macrophage phenotypes induced by amiodarone also displayed characteristics of proinflammatory macrophages (significantly increased nitric oxide production, increased vacuolation and lipidosis and decreased phagocytosis). In contrast, staurosporine treatment resulted in increased NO production, as well as arginase-1 activity.ConclusionHigh content image analysis was able to determine distinct differences in morphology between non-activated and LPS/IFN-γ activated macrophages, characterized by increased vacuolation and lipidosis. When exposed to compounds that induce a FAM phenotype, healthy non-activated macrophages displayed proinflammatory (amiodarone) or pro-apoptotic (staurosporine) characteristics but these responses were independent of a change in activation status. This technique could be applied in early drug discovery safety assessment to identify immune responses earlier and increase the understanding of alveolar macrophage responses to new molecules challenge in development of new inhalation therapies, which in turn will enhance decision-making in an early safety assessment of novel drug candidates.

Applications of Biophysics in Early Drug Discovery

Applications of Biophysics in Early Drug Discovery

Assembly of Cell Mimicking Supported and Suspended Lipid Bilayer Models for the Study of Molecular Interactions.

Model cell membranes are a useful screening tool with applications ranging from early drug discovery to toxicity studies. The cell membrane is a crucial protective barrier for all cell types, separating the internal cellular components from the extracellular environment. These membranes are composed largely of a lipid bilayer, which contains outer hydrophilic head groups and inner hydrophobic tail groups, along with various proteins and cholesterol. The composition and structure of the lipids themselves play a crucial role in regulating biological function, including interactions between cells and the cellular microenvironment, which may contain pharmaceuticals, biological toxins, and environmental toxicants. In this study, methods to formulate uni-lipid and multi-lipid supported and suspended cell mimicking lipid bilayers are described. Previously, uni-lipid phosphatidylcholine (PC) lipid bilayers as well as multi-lipid placental trophoblast-inspired lipid bilayers were developed for use in understanding molecular interactions. Here, methods for achieving both types of bilayer models will be presented. For cell mimicking multi-lipid bilayers, the desired lipid composition is first determined via lipid extraction from primary cells or cell lines followed by liquid chromatography-mass spectrometry (LC-MS). Using this composition, lipid vesicles are fabricated using a thin-film hydration and extrusion method and their hydrodynamic diameter and zeta potential are characterized. Supported and suspended lipid bilayers can then be formed using quartz crystal microbalance with dissipation monitoring (QCM-D) and on a porous membrane for use in a parallel artificial membrane permeability assay (PAMPA), respectively. The representative results highlight the reproducibility and versatility of in vitro cell membrane lipid bilayer models. The methods presented can aid in rapid, facile assessment of the interaction mechanisms, such as permeation, adsorption, and embedment, of various molecules and macromolecules with a cell membrane, helping in the screening of drug candidates and prediction of potential cellular toxicity.

Evaluation of Normothermic Machine Perfusion of Porcine Livers as a Novel Preclinical Model to Predict Biliary Clearance and Transporter-Mediated Drug-Drug Interactions Using Statins.

There is a lack of translational preclinical models that can predict hepatic handling of drugs. In this study, we aimed to evaluate the applicability of normothermic machine perfusion (NMP) of porcine livers as a novel ex vivo model to predict hepatic clearance, biliary excretion, and plasma exposure of drugs. For this evaluation, we dosed atorvastatin, pitavastatin, and rosuvastatin as model drugs to porcine livers and studied the effect of common drug-drug interactions (DDIs) on these processes. After 120 minutes of perfusion, 0.104 mg atorvastatin (n = 3), 0.140 mg pitavastatin (n = 5), or 1.4 mg rosuvastatin (n = 4) was administered to the portal vein, which was followed 120 minutes later by a second bolus of the statin coadministered with OATP perpetrator drug rifampicin (67.7 mg). After the first dose, all statins were rapidly cleared from the circulation (hepatic extraction ratio > 0.7) and excreted into the bile. Presence of human-specific atorvastatin metabolites confirmed the metabolic capacity of porcine livers. The predicted biliary clearance of rosuvastatin was found to be closer to the observed biliary clearance. A rank order of the DDI between the various systems upon coadministration with rifampicin could be observed: atorvastatin (AUC ratio 7.2) > rosuvastatin (AUC ratio 3.1) > pitavastatin (AUC ratio 2.6), which is in good agreement with the clinical DDI data. The results from this study demonstrated the applicability of using NMP of porcine livers as a novel preclinical model to study OATP-mediated DDI and its effect on hepatic clearance, biliary excretion, and plasma profile of drugs. SIGNIFICANCE STATEMENT: This study evaluated the use of normothermic machine perfusion (NMP) of porcine livers as a novel preclinical model to study hepatic clearance, biliary excretion, plasma (metabolite) profile of statins, and OATP-mediated DDI. Results showed that NMP of porcine livers is a reliable model to study OATP-mediated DDI. Overall, the rank order of DDI severity indicated in these experiments is in good agreement with clinical data, indicating the potential importance of this new ex vivo model in early drug discovery.

Acoustic Ejection Mass Spectrometry: A Fully Automatable Technology for High-Throughput Screening in Drug Discovery.

Acoustic droplet ejection (ADE)-open port interface (OPI)-mass spectrometry (MS) has recently been introduced as a versatile analytical method that combines fast and contactless acoustic sampling with sensitive and accurate electrospray ionization (ESI)-MS-based analyte detection. The potential of the technology to provide label-free measurements in subsecond analytical cycle times makes it an attractive option for high-throughput screening (HTS). Here, we report the first implementation of ADE-OPI-MS in a fully automated HTS environment, based on the example of a biochemical assay aiming at the identification of small-molecule inhibitors of the cyclic guanosine monophosphate-adenosine monophosphate (GMP-AMP) synthase (cGAS). First, we describe the optimization of the method to enable sensitive and accurate determination of enzyme activity and inhibition in miniaturized 1536-well microtiter plate format. Then we show both results from a validation single-concentration screen using a test set of 5500 compounds, and the subsequent concentration-response testing of selected hits in direct comparison with a previously established matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) readout. Finally, we present the development of an in-line OPI cleaning procedure aiming to match the instrument robustness required for large-scale HTS campaigns. Overall, this work points to critical method development parameters and provides guidance for the establishment of integrated ADE-OPI-MS as HTS-compatible technology for early drug discovery.

BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space.

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).

Combining High‐Throughput Synthesis and High‐Throughput Protein Crystallography for Accelerated Hit Identification

AbstractProtein crystallography (PX) is widely used to drive advanced stages of drug optimization or to discover medicinal chemistry starting points by fragment soaking. However, recent progress in PX could allow for a more integrated role into early drug discovery. Here, we demonstrate for the first time the interplay of high throughput synthesis and high throughput PX. We describe a practical multicomponent reaction approach to acrylamides and ‐esters from diverse building blocks suitable for mmol scale synthesis on 96‐well format and on a high‐throughput nanoscale format in a highly automated fashion. High‐throughput PX of our libraries efficiently yielded potent covalent inhibitors of the main protease of the COVID‐19 causing agent, SARS‐CoV‐2. Our results demonstrate, that the marriage of in situ HT synthesis of (covalent) libraires and HT PX has the potential to accelerate hit finding and to provide meaningful strategies for medicinal chemistry projects.

Combining High-Throughput Synthesis and High-Throughput Protein Crystallography for Accelerated Hit Identification.

Protein crystallography (PX) is widely used to drive advanced stages of drug optimization or to discover medicinal chemistry starting points by fragment soaking. However, recent progress in PX could allow for a more integrated role into early drug discovery. Here, we demonstrate for the first time the interplay of high throughput synthesis and high throughput PX. We describe a practical multicomponent reaction approach to acrylamides and ‐esters from diverse building blocks suitable for mmol scale synthesis on 96‐well format and on a high‐throughput nanoscale format in a highly automated fashion. High‐throughput PX of our libraries efficiently yielded potent covalent inhibitors of the main protease of the COVID‐19 causing agent, SARS‐CoV‐2. Our results demonstrate, that the marriage of in situ HT synthesis of (covalent) libraires and HT PX has the potential to accelerate hit finding and to provide meaningful strategies for medicinal chemistry projects.

Abstract LB123: A high value pharmacological platform dedicated to the real time study of stimulatory immune checkpoint signaling pathways

Abstract Immune checkpoint (ICP) co-receptors play a key role in the fine modulation of the immune response. While inhibitory ICP co-receptors are mainly characterized in cancer by the exhaustion of the immune response, stimulatory ICP co-receptors are clearly involved in an anarchic activation of the immune system leading to autoimmune diseases. But in another hand, this subtype of co-receptor shows a great asset in cancer therapy by increasing anti-tumoral response. All together, these co-receptors show a great interest for the development of innovative therapies in immunology or immuno-oncology. Nowadays, treatments targeting these co-receptors are currently tested in clinical trials or approved for use in man, especially antibodies targeting both inhibitory and stimulatory ICP. Small molecule-based strategy is tested in early drug discovery and in early clinical trials. In order to screen and assess the potency and efficiency of these therapies, robust and reliable cell-based assays are urgently needed. At Domain Therapeutics, using our powerful proprietary BRET technology: bioSens-AllTM, we successfully develop and validate a cell-based assay platform to assess immune checkpoint pharmacology. The activation or blockade of the signaling of checkpoint co-receptors are monitored in real time and in living cells. BRET assays dedicated to stimulatory ICPs: 4-1BB/4-1BB Ligand and OX-40/OX-40 Ligand were designed. BRET assay development is inspired by the natural recruitment and proximity of specific cytoplasmic partners very close to their co-receptors in an activated pathway. Indeed, the first assay design is based on the co-receptor fused with rGFP and its cytoplasmic partner fused with a rLucII. In the second assay design, the co-receptor is no longer fused, but is co-transfected with a membrane anchor fused with the rGFP and co-transfected with the cytoplasmic partner fused with rLucII. So, in an activated system (soluble ligand, antibody or in co-culture system) a modulation of BRET can be observed. For ICP assays dedicated to 4-1BB, BRET signal is triggered using soluble ligand, agonist antibody, or co-culture where TRAF1 is the biosensor fused with rLucII. Further validation was obtained for OX-40/OX-40 Ligand. Those BRET assays complement once previously developed for inhibitory ICPs axis: PD-1/PD-Ligand 1 or PD-Ligand 2 and CTLA-4/CD80-CD86. The ICP Platform set up here shows a good accuracy and robustness and represents a strong and reliable technology for drug discovery dedicated to ICPs. Our spatio-temporal cell-based functional assays can support broad drug programs, including: High Throughput Functional Screening, Lead Optimization and Bioanalytical QC lot Release. Citation Format: Alice GENTIL DIT MAURIN, Christel FRANCHET, Stephan SCHANN, Xavier LEROY. A high value pharmacological platform dedicated to the real time study of stimulatory immune checkpoint signaling pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB123.

Abstract 1650: MoA-based reporter bioassays enable quantitative potency determination for therapeutic biologics targeting co-stimulatory and co-inhibitory immune checkpoint receptors

Abstract Immune checkpoint (IC) receptors play critical roles in maintaining immune homeostasis and are emerged as promising drug targets for the treatment of various cancers and autoimmune disorders. Monoclonal antibodies designed to block immune inhibitory receptors PD-1 and CTLA-4 have been approved and showed unprecedented efficacy in cancer treatment. This led to rapid expansion of drug development to many more IC receptors targeted individually or in combination. However, due to the novelty and fast speed, one of the bottleneck during immunotherapy drug development is the lack of quantitative and reproducible functional assays for potency determination when existing methods heavily rely on primary immune cells and the process are labor intensive and highly variable. To address this need, we developed a panel of cell-based reporter bioassays that can quantitatively measure the potencies of biologics targeting individual IC receptor (e.g. PD-1, LAG-3, BTLA, CTLA-4, TIGIT, TIM-3, ICOS, 4-1BB, CD40), or bispecific molecules targeting two IC receptors spontaneously (e.g. PD-1xTIGIT, PD-1xLAG-3, PD-1xCTLA-4, PD1x4-1BB). These bioassays each consist of an engineered T effector cell line that expresses T cell receptors and IC receptors of interest, and an engineered artificial antigen presenting cells (aAPC) expressing the corresponding ligands for the IC receptors. The T effector cell line also expresses a luciferase reporter driven by response elements/promoter specifically responding to TCR activation which can be co-stimulated or inhibited by the signal from the IC receptors. These bioassays are designed to reflect the mechanisms of action for the drug candidates designed for each IC receptor and the assay signals are specific. They are prequalified according to ICH guidelines and show the precision, accuracy and linearity required for relative potency determination and product stability study under cGMP environments. Therefore, these MoA-based bioassays can serve as value tools for early drug discovery, potency determination and stability study throughout immunotherapy drug development. Citation Format: Jamison Grailer, Julia Gilden, Pete Stecha, Denise Garvin, Jun Wang, michael Beck, Jim Hartnett, Frank Fan, mei cong, Zhi-Jie Jey Cheng. MoA-based reporter bioassays enable quantitative potency determination for therapeutic biologics targeting co-stimulatory and co-inhibitory immune checkpoint receptors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1650.