Discovery Screening Research Articles

A growth-based platform for detecting domain–peptide interactions in the cytoplasm of mammalian cells

Development of a method for detecting protein–protein interactions (PPIs) in living cells is important for therapeutic drug screening against various diseases including infectious diseases. We have recently developed a method named SOS localization-based interaction screening (SOLIS), in which we designed membrane-anchored and SOS-fused chimeric proteins, whose PPI-dependent association triggers membrane localization of the SOS-fused chimeric protein, activates the Ras/MAPK pathway, and induces cell growth. While SOLIS was able to detect relatively strong PPIs, further sensitivity was required for detecting intracellular endogenous PPIs typically having a micromolar order of dissociation constant (Kd). Here we develop high-sensitive SOLIS (H-SOLIS) that could universally detect PPIs with lower affinities. In order to improve the sensitivity, H-SOLIS introduces a heterodimeric helper interaction, in which addition of a small-molecule helper ligand could accommodate association of the two chimeric proteins and regulate the sensitivity. Four types of domain–peptide interactions having known Kd values are employed to examine the versatility and detection limit of H-SOLIS. Consequently, the heterodimer-inducible helper ligand dramatically enhances detection sensitivity, lowering the detection limit to a ten-micromolar order of Kd. Thus, H-SOLIS could be a platform to detect disease-related domain–peptide interactions for drug discovery screening.

Multi-target-based polypharmacology prediction (mTPP): An approach using virtual screening and machine learning for multi-target drug discovery

Polypharmacology has become a new paradigm in drug discovery and plays an increasingly vital role in discovering multi-target drugs. In this context, multi-target drugs are a promising approach to treating polygenic diseases. Many in-silico prediction methods have been developed to screen active molecules acting on multiple targets. The relationship between the action of multiple targets and the drug's overall efficacy is significant for developing multi-target drugs. So, the prediction method for this relationship urgently needs to be developed. This paper introduces multi-target-based polypharmacology prediction (mTPP), an approach using virtual screening and machine learning to explore the relationship. To predict the activity of the potential hepatoprotective components, the data on the binding strength of a single ingredient with multiple targets and the proliferation rate of the compounds against acetaminophen (APAP)-induced injury L02 cells were all used to construct the mTPP model by Multi-layer Perceptron (MLP), Support Vactor Regression (SVR), Decision Tree Regressor (DTR), and Gradient Boost Regression (GBR) algorithms. Compared with MLP, SVR, and DTR algorithms, GBR algorithms showed the best performance with R2test = 0.73 and EVtest = 0.75. In addition, 20 candidates with potential effects against drug-induced liver injury (DILI) were predicted by the mTPP model. Furthermore, 2 of the 20 candidates, Chelerythrine and Biochanin A, were applied to evaluate the model's accuracy. The results showed that Chelerythrine and Biochanin A could improve the viability of APAP-induced injury cells. Thus, the mTPP model is hoped to help develop polypharmacology and discover multi-target drugs.

Adipocyte-based high throughput screening for anti-obesity drug discovery: Current status and future perspectives.

Drug discovery for obesity treatment, particularly bodily slimming, is a topic of timely importance that requires continued investigation, as the current therapies have limited efficacy with many adverse effects. Obesity is associated with adipose tissue expansion, where the size and number of adipocytes increase. Over the past few decades, high-throughput/content screening (HTS/HCS) has been carried out on morphological changes in adipose tissues and adipocytes for the development of anti-obesity therapies. Increased understating of current adipocyte-based HTS/HCS technology will facilitate drug screening for obesity and weight control.

Multi-Level High-Throughput Screening for Discovery of Ligands That Inhibit Insulin Aggregation.

We have developed a multi-level virtual screening protocol to identify lead molecules from the FDA inactives database that can inhibit insulin aggregation. The method is based on the presence of structural and interaction specificity in non-native aggregation pathway protein-protein interactions. Some key challenges specific to the present problem, when compared with native protein association, include structural heterogeneity of the protein species involved, multiple association pathways, and relatively higher probability of conformational rearrangement of the association complex. In this multi-step method, the inactives database was first screened using the dominant pharmacophore features of previously identified molecules shown to significantly inhibit insulin aggregation nucleation by binding to its aggregation-prone conformers. We then performed ensemble docking of several low-energy ligand conformations on these aggregation-prone conformers followed by molecular dynamics simulations and binding affinity calculations on a subset of docked complexes to identify a final set of five potential lead molecules to inhibit insulin aggregation nucleation. Their effect on aggregation inhibition was extensively investigated by incubating insulin under aggregation-prone aqueous buffer conditions (low pH, high temperature). Aggregation kinetics were characterized using size exclusion chromatography and Thioflavin T fluorescence assay, and the secondary structure was determined using circular dichroism spectroscopy. Riboflavin provided the best aggregation inhibition, with 85% native monomer retention after 48 h incubation under aggregation-prone conditions, whereas the no-ligand formulation showed complete monomer loss after 36 h. Further, insulin incubated with two of the screened inactives (aspartame, riboflavin) had the characteristic α-helical dip in CD spectra, while the no-ligand formulation showed a change to β-sheet rich conformations.

Insights into performance evaluation of compound-protein interaction prediction methods.

Machine-learning-based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing. Despite numerous recent publication with increasing methodological sophistication claiming consistent improvements in predictive accuracy, we have observed a number of fundamental issues in experiment design that produce overoptimistic estimates of model performance. We systematically analyze the impact of several factors affecting generalization performance of CPI predictors that are overlooked in existing work: (i) similarity between training and test examples in cross-validation; (ii) synthesizing negative examples in absence of experimentally verified negative examples and (iii) alignment of evaluation protocol and performance metrics with real-world use of CPI predictors in screening large compound libraries. Using both state-of-the-art approaches by other researchers as well as a simple kernel-based baseline, we have found that effective assessment of generalization performance of CPI predictors requires careful control over similarity between training and test examples. We show that, under stringent performance assessment protocols, a simple kernel-based approach can exceed the predictive performance of existing state-of-the-art methods. We also show that random pairing for generating synthetic negative examples for training and performance evaluation results in models with better generalization in comparison to more sophisticated strategies used in existing studies. Our analyses indicate that using proposed experiment design strategies can offer significant improvements for CPI prediction leading to effective target compound screening for drug repurposing and discovery of putative chemical ligands of SARS-CoV-2-Spike and Human-ACE2 proteins. Code and supplementary material available at https://github.com/adibayaseen/HKRCPI. Supplementary data are available at Bioinformatics online.

Silybin A enhances circadian clock by targeting CRY1 and disrupting its interaction with CLOCK

Circadian clock governs a large array of physiological and metabolic functions. The amplitude decline of circadian rhythm is associated with various chronic diseases. However, few clock enhancing-molecules are known. CRY1 is one of the main components of the core circadian clock and its binding with CLOCK leads to decreased transcriptional activity. Thus, compounds interfering with CRY1-CLOCK interaction may enhance circadian rhythm. Natural products are rich sources of drug discovery and herb-based virtual screen provides an efficient way to identify active herbs. Here, by performing herb-based virtual screen against the CLOCK interacting pocket in CRY1 and cell-based assays, we found that Silybum marianum, a widely used medical plant, can activate circadian clock transcription. Molecular level studies showed that its extract silymarin can bind to CRY1 and disrupt CRY1-CLOCK interaction. Further computational and experimental analysis revealed that silybin A is the main bioactive compound that enhances the amplitude of circadian rhythm by reducing the negative arm of the transcription/translation feedback loop. Silybum marianum and its main extract, silymarin, were reported to have hepatoprotective, cardiovascular-protective, antidiabetic, anti-inflammatory, antioxidative, and anti-cancer effects with unclear mechanism. Our study revealed that silymarin and the purified compound silybin A may perform their pharmacological functions by modulating circadian clock. We also demonstrate that disrupting CRY1-CLOCK interaction provides a new route for upregulating circadian rhythm and the CLOCK binding site in CRY1 serves as a druggable pocket for discovering clock enhancing molecules for treating related diseases.

Systemic injection of nicotinic acetylcholine receptor antagonist mecamylamine affects licking, eyelid size, and locomotor and autonomic activities but not temporal prediction in male mice

Nicotinic acetylcholine receptors are thought to be associated with a wide range of phenomena, such as movement, learning, memory, attention, and addiction. However, the causal relationship between nicotinic receptor activity and behavior remains unclear. Contrary to the studies that examined the functions of muscarinic acetylcholine receptors, the role of the nicotinic acetylcholine receptors on behavior has not been examined as extensively. Here, we examined the effects of intraperitoneal injection of mecamylamine, a nicotinic acetylcholine receptor antagonist, on the performance of male mice in a head-fixed temporal conditioning task and a free-moving open-field task. The head-fixed experimental setup allowed us to record and precisely quantify the licking response while the mice performed the behavioral task with no external cues. In addition, by combining the utility of the head-fixed experimental design with computer vision analysis based on deep learning algorithms, we succeeded in quantifying the eyelid size of awake mice. In the temporal conditioning task, we delivered a 10% sucrose solution every 10 s using a blunt-tipped needle placed within the licking distance of the mice. After the training, the mice showed increased anticipatory licking toward the timing of sucrose delivery, suggesting that the mice could predict the timing of the reward. Systemic injection of mecamylamine decreased licking behavior and caused eye closure but had no effect on learned conditioned predictive behavior in the head-fixed temporal conditioning task. In addition, the injection of mecamylamine decreased spontaneous locomotor activity in a dose-dependent manner in the free-moving open-field task. The results in the open-field experiments further revealed that the effect of mecamylamine on fecal output and urination, suggesting the effects on autonomic activities. Our achievement of successful eyelid size recording has potential as a useful approach in initial screening for drug discovery. Our study paves a way forward to understanding the role of nicotinic acetylcholine receptors on learning and behavior.

In vivo fluorescent screening for HPPD-targeted herbicide discovery.

4-Hydroxyphenylpyruvate dioxygenase (HPPD), playing a critical role in vitamin E and plastoquinone biosynthesis in plants, has been recognized as one of the most important targets for herbicide discovery for over 30 years. Structure-based rational design of HPPD inhibitors has received more and more research interest. However, a critical challenge in the discovery of new HPPD inhibitors is the common inconsistency between molecular-level HPPD-based bioevaluation and the weed control efficiency in fields, due to the unpredictable biological processes of absorption, distribution, metabolism, and excretion. In this study, we developed a fluorescent-sensing platform of efficient in vivo screening for HPPD-targeted herbicide discovery. The refined sensor has good capability of in situ real-time fluorescence imaging of HPPD in living cells and zebrafish. More importantly, it enabled the direct visible monitoring of HPPD inhibition in plants in a real-time manner. We developed a highly efficient in vivo fluorescent screening method for HPPD-targeted herbicide discovery. This discovery not only offers a promising tool to advance HPPD-targeted herbicide discovery, but it also demonstrates a general path to develop the highly efficient, target-based, in vivo screening for pesticide discovery. © 2022 Society of Chemical Industry.

Free energy perturbation-based large-scale virtual screening for effective drug discovery against COVID-19.

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE–based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

Nutrient Starvation Exposure Induced the Down-Regulation of Stress Responsive Genes and Selected Bioactive Metabolic Pathways in Phaeodactylum tricornutum

The microalga Phaeodactylum tricornutum is considered a model diatom. It is the second diatom whose genome was sequenced and the first one genetically engineered. This permits its use as a cell factory for the production of high-value compounds for nutraceutical, cosmeceutical, pharmaceutical, biodiesel, and bioplastic applications. This study is focused on analyzing expression levels of enzymes involved in the synthesis of sulfoglycolipids and monogalactosyldiacylglycerols, compounds known to have anticancer and immunomodulatory activities, and genes coding antioxidant, heat shock and stress-responsive proteins, in various culturing conditions. Our data showed that both nutrient starvation and senescence induced the down-regulation of both sulfoglycolipid and monogalactosyldiacylglycerol synthesis-related genes and stress-responsive genes (compared to the replete condition), suggesting that the control condition, consisting of cells in the exponential phase in replete medium, is the condition with the highest expression of the genes of interest and worth of further bioactivity screening and chemical analyses for drug discovery and biotechnological applications.

An immunogenic cell injury module for the single-cell multiplexed activity metabolomics platform to identify promising anti-cancer natural products

Natural products constitute and significantly impact many current anti-cancer medical interventions. A subset of natural products induces injury processes in malignant cells that recruit and activate host immune cells to produce an adaptive anti-cancer immune response, a process known as immunogenic cell death. However, a challenge in the field is to delineate forms of cell death and injury that best promote durable antitumor immunity. Addressing this with a single-cell chemical biology natural product discovery platform, like multiplex activity metabolomics, would be especially valuable in human leukemia, where cancer cells are heterogeneous and may react differently to the same compounds. Herein, a new ten-color, fluorescent cell barcoding-compatible module measuring six immunogenic cell injury signaling readouts are as follows: DNA damage response (γH2AX), apoptosis (cCAS3), necroptosis (p-MLKL), mitosis (p-Histone H3), autophagy (LC3), and the unfolded protein response (p-EIF2α). A proof-of-concept screen was performed to validate functional changes in single cells induced by secondary metabolites with known mechanisms within bacterial extracts. This assay was then applied in multiplexed activity metabolomics to reveal an unexpected mammalian cell injury profile induced by the natural product narbomycin. Finally, the functional consequences of injury pathways on immunogenicity were compared with three canonical assays for immunogenic hallmarks, ATP, HMGB1, and calreticulin, to correlate secondary metabolite-induced cell injury profiles with canonical markers of immunogenic cell death. In total, this work demonstrated a new phenotypic screen for discovery of natural products that modulate injury response pathways that can contribute to cancer immunogenicity.

High Throughput Screening for Drug Discovery and Virus Detection.

High throughput screening systems are automated labs for the analysis of many biochemical substances in the drug discovery and virus detection process. This paper was motivated by the problem of automating testing for viruses and new drugs using high throughput screening systems. The emergence of severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) at the turn of 2019-2020 presented extraordinary challenges to public health. Existing approaches to test viruses and new drugs do not use optimal schedules and are not efficient. The scheduling of activities performed by various resources in a high throughput screening system affects its efficiency, throughput, operations cost, and quality of screening. This study aims to minimize the total screening (flow) time and ensure the consistency and quality of screening. This paper develops innovative mixed-integer models that efficiently compute optimal schedules for screening many microplates to identify new drugs and determine whether samples contain viruses. The methods integrate job-shop and cyclic scheduling. Experiments are conducted for a drug discovery process of screening an enzymatic assay and a general process of detecting SARS-CoV-2. The method developed in this article can reduce screening time by as much as 91.67%. The optimal schedules for high throughput screening systems greatly reduce the total flow time and can be computed efficiently to help discover new drugs and detect viruses.

Biomacromolecule-Assisted Screening for Reaction Discovery and Catalyst Optimization.

Reaction discovery and catalyst screening lie at the heart of synthetic organic chemistry. While there are efforts at de novo catalyst design using computation/artificial intelligence, at its core, synthetic chemistry is an experimental science. This review overviews biomacromolecule-assisted screening methods and the follow-on elaboration of chemistry so discovered. All three types of biomacromolecules discussed─enzymes, antibodies, and nucleic acids─have been used as "sensors" to provide a readout on product chirality exploiting their native chirality. Enzymatic sensing methods yield both UV-spectrophotometric and visible, colorimetric readouts. Antibody sensors provide direct fluorescent readout upon analyte binding in some cases or provide for cat-ELISA (Enzyme-Linked ImmunoSorbent Assay)-type readouts. DNA biomacromolecule-assisted screening allows for templation to facilitate reaction discovery, driving bimolecular reactions into a pseudo-unimolecular format. In addition, the ability to use DNA-encoded libraries permits the barcoding of reactants. All three types of biomacromolecule-based screens afford high sensitivity and selectivity. Among the chemical transformations discovered by enzymatic screening methods are the first Ni(0)-mediated asymmetric allylic amination and a new thiocyanopalladation/carbocyclization transformation in which both C-SCN and C-C bonds are fashioned sequentially. Cat-ELISA screening has identified new classes of sydnone-alkyne cycloadditions, and DNA-encoded screening has been exploited to uncover interesting oxidative Pd-mediated amido-alkyne/alkene coupling reactions.

Confidence bands and hypothesis tests for hit enrichment curves

In virtual screening for drug discovery, hit enrichment curves are widely used to assess the performance of ranking algorithms with regard to their ability to identify early enrichment. Unfortunately, researchers almost never consider the uncertainty associated with estimating such curves before declaring differences between performance of competing algorithms. Uncertainty is often large because the testing fractions of interest to researchers are small. Appropriate inference is complicated by two sources of correlation that are often overlooked: correlation across different testing fractions within a single algorithm, and correlation between competing algorithms. Additionally, researchers are often interested in making comparisons along the entire curve, not only at a few testing fractions. We develop inferential procedures to address both the needs of those interested in a few testing fractions, as well as those interested in the entire curve. For the former, four hypothesis testing and (pointwise) confidence intervals are investigated, and a newly developed EmProc approach is found to be most effective. For inference along entire curves, EmProc-based confidence bands are recommended for simultaneous coverage and minimal width. While we focus on the hit enrichment curve, this work is also appropriate for lift curves that are used throughout the machine learning community. Our inferential procedures trivially extend to enrichment factors, as well.

Atrial-specific hiPSC-derived cardiomyocytes in drug discovery and disease modeling.

The discovery and application of human-induced pluripotent stem cells (hiPSCs) have been instrumental in the investigation of the pathophysiology of cardiovascular diseases. Patient-specific hiPSCs can now be generated, genome-edited, and subsequently differentiated into various cell types and used for regenerative medicine, disease modeling, drug testing, toxicity screening, and 3D tissue generation. Modulation of the retinoic acid signaling pathway has been shown to direct cardiomyocyte differentiation towards an atrial lineage. A variety of studies have successfully differentiated patient-specific atrial cardiac myocytes (hiPSC-aCM) and atrial engineered heart tissue (aEHT) that express atrial specific genes (e.g., sarcolipin and ANP) and exhibit atrial electrophysiological and contractility profiles. Identification of protocols to differentiate atrial cells from patients with atrial fibrillation and other inherited diseases or creating disease models using genetic mutation studies has shed light on the mechanisms of atrial-specific diseases and identified the efficacy of atrial-selective pharmacological compounds. hiPSC-aCMs and aEHTs can be used in drug discovery and drug screening studies to investigate the efficacy of atrial selective drugs on atrial fibrillation models. Furthermore, hiPSC-aCMs can be effective tools in studying the mechanism, pathophysiology and treatment options of atrial fibrillation and its genetic underpinnings. The main limitation of using hiPSC-CMs is their immature phenotype compared to adult CMs. A wide range of approaches and protocols are used by various laboratories to optimize and enhance CM maturation, including electrical stimulation, culture time, biophysical cues and changes in metabolic factors.

Death-Associated Protein Kinase 3 Inhibitors Identified by Virtual Screening for Drug Discovery in Cancer and Hypertension.

Death-associated protein kinase 3 (DAPK3) is a serine/threonine protein kinase that regulates apoptosis, autophagy, transcription, and actin cytoskeleton reorganization. DAPK3 induces morphological alterations in apoptosis when overexpressed, and it is considered a potential drug target in antihypertensive and anticancer drug development. In this article, we report new findings from a structure-guided virtual screening for discovery of phytochemicals that could modulate the elevated expression of DAPK3, and with an eye to anticancer drug discovery. We used the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT), a curated database, as part of the methodology. The potential initial hits were identified based on their physicochemical properties and binding affinity toward DAPK3. Subsequently, various filters for drug likeness followed by interaction analysis and molecular dynamics (MD) simulations for 100 nsec were performed to explore the conformational sampling and stability of DAPK3 with the candidate molecules. Notably, the data from all-atom MD simulations and principal component analysis suggested that DAPK3 forms stable complexes with ketanserin and rotenone. In conclusion, this study supports the idea that ketanserin and rotenone bind to DAPK3, and show stability, which can be further explored as promising scaffolds in drug development and therapeutics innovation in clinical contexts such as hypertension and various types of cancer.

Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer.

NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound 762 exhibited excellent binding energy of −42.67 kJ/mol, better than Dabrafenib (−33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein–ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7–Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.

Complex Feline Disease Mapping Using a Dense Genotyping Array.

The current feline genotyping array of 63 k single nucleotide polymorphisms has proven its utility for mapping within breeds, and its use has led to the identification of variants associated with Mendelian traits in purebred cats. However, compared to single gene disorders, association studies of complex diseases, especially with the inclusion of random bred cats with relatively low linkage disequilibrium, require a denser genotyping array and an increased sample size to provide statistically significant associations. Here, we undertook a multi-breed study of 1,122 cats, most of which were admitted and phenotyped for nine common complex feline diseases at the Cornell University Hospital for Animals. Using a proprietary 340 k single nucleotide polymorphism mapping array, we identified significant genome-wide associations with hyperthyroidism, diabetes mellitus, and eosinophilic keratoconjunctivitis. These results provide genomic locations for variant discovery and candidate gene screening for these important complex feline diseases, which are relevant not only to feline health, but also to the development of disease models for comparative studies.

Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM

Sequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Here we introduce an active learning method that identifies minimal but highly informative gene sets that enable the identification of cell types, physiological states and genetic perturbations in single-cell data using a small number of genes. Our active feature selection procedure generates minimal gene sets from single-cell data by employing an active support vector machine (ActiveSVM) classifier. We demonstrate that ActiveSVM feature selection identifies gene sets that enable ~90% cell-type classification accuracy across, for example, cell atlas and disease-characterization datasets. The discovery of small but highly informative gene sets should enable reductions in the number of measurements necessary for application of single-cell mRNA-seq to clinical tests, therapeutic discovery and genetic screens.

P-125 Phase 1b study of RGX-202-01, a first-in-class oral inhibitor of the SLC6A8/CKB pathway, in combination with FOLFIRI and bevacizumab (BEV) in second-line advanced colorectal cancer (CRC)

A proprietary in vivo target discovery screen identified creatine kinase-B (CKB) as a cancer driver in KRAS mutant (KRAS-mut) CRC. CKB promotes tumor growth and survival under hypoxia. CKB generates the energetic metabolite phospho-creatine (PCr), which is imported into cells through the creatine transporter, SLC6A8. PCr generates intracellular ATP that enables tumoral survival. RGX-202-01 is a small molecule inhibitor of SLC6A8 that depletes intracellular PCr and ATP, resulting in apoptosis. In a completed phase 1a study, RGX-202-01 monotherapy demonstrated objective anti-tumor activity in the relapsed/refractory KRAS-mut CRC setting without dose-limiting toxicity. The objectives of this ongoing phase 1b study are to evaluate safety, PK/PD, and efficacy of RGX-202-01 in combination with standard-of-care (SOC) FOLFIRI + BEV in second-line CRC, a setting where SOC therapy results in an ORR and mPFS of approximately 15% and 6 months, respectively. Subjects with advanced CRC who had disease progression after receiving a first-line oxaliplatin-containing regimen were eligible. As of 02-24-2022, 17 patients (pts) have been enrolled. 8 pts were enrolled in 2 dose escalation cohorts of RGX-202-01: 2400mg BID (4 pts) and 3000mg BID (4 pts) combined with FOLFIRI and BEV. 9 pts were enrolled in the dose expansion phase of the study and received RGX-202-01 3000mg BID combined with FOLFIRI and BEV. No DLTs were observed and the MTD was not reached in the dose escalation phase. Most common G2 TRAEs were nausea and diarrhea (50%) and most common G3 TRAEs were fatigue (12%) and hypertension (12%) There were no Grade 4-5 TRAEs. At the RP2D of 3000 mg BID, PK analysis showed sustained RGX-202-01 drug exposures above target levels (IC50). In addition, Serum and urine creatine measurements indicated robust SLC6A8 target inhibition (in both escalation cohort). 9 pts had KRAS-mut CRC, and all were evaluable for RECIST 1.1 response. 5 of these pts had PR (confirmed) and 4 pts have SD as best response (ORR 56%, DCR 100%). 5 of these pts remain on study for a duration ranging from 15-58 weeks. One patient with a PR at 16 weeks had surgery with curative intent and was observed off therapy with no evidence of disease until week 44 when a CT identified a recurrence. 8 pts had KRAS WT CRC. Of the 5 KRAS WT pts evaluable for response, 1 has a PR on first scan and is ongoing and 4 pts have SD as best response. (ORR 20%, DCR 100%)3 patients remain on study at 8, 20 and 24 weeks respectively. RGX-202-01 combined with FOLFIRI and BEV was well tolerated with no DLTs at the dose levels evaluated which induced potent inhibition of SLC6A8. Encouraging efficacy was observed in patients with RAS mutate mCRC, with 56% response rate exceeding that expected with SOC. Preferential activity in patients with RAS mutated tumors is consistent with pre-clinical studies. Enrollment in the expansion phase continues and a phase 2/3 trial in 2L CRC with RAS mut is planned.