Challenges In Drug Development Research Articles

Modeling doxorubicin-induced cardiotoxicity with human myocardial tissue slices: insights on genetic susceptibility and cardioprotection

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): PSIDER and HARVEY (ZonMW) Introduction Cardiotoxicity poses a significant challenge in drug development, often leading to the withdrawal of approved drugs. To address this issue, there is a critical need for improved models to predict cardiac adverse effects. In line with the imperative for animal-free preclinical testing, this study aimed to develop a novel model using human myocardial tissue slices to get a deeper understanding and potential prevention of doxorubicin-Induced cardiotoxicity. Methods Human myocardial tissue slices of 300 μm thick were derived from end-stage heart failure patients explanted hearts, including those harbouring likely or pathogenic cardiovascular mutations (class 4 and 5) (N=3) and non-genetic cardiovascular etiology (N=3). Myocardial tissue slices were cultured under physiological mechanical and electrical conditions. Slices were exposed to doxorubicin (1 µM) over a 10-day culture period, and their responses were compared to DMSO-vehicle controls, by recording contractile force and Ca2+ transients. Additionally, a subset of slices was treated with a cardioprotective agent, dexrazoxane (100 µM), 1 day prior to and during doxorubicin exposure to assess potential cardioprotection. Results Doxorubicin-exposed slices with a pathogenic mutation exhibited reduced mechanical force, increased threshold potential, and impaired ability to follow pacing at higher frequencies. Additionally, aberrations in calcium handling, manifested as arrhythmia-like disconnection of muscle fibers, were notably observed in these slices. Instead, slices from patients without pathogenic mutations showed minimal cardiotoxicity when exposed to doxorubicin. Encouragingly, pretreatment with dexrazoxane demonstrated a protective effect against doxorubicin-induced cardiotoxicity in the genetically susceptible group. Our functional findings corroborated our structural data, highlighting the breakdown of sarcomere structure and cell-cell connections and increased DNA damage, particularly in patients harbouring a genetic cardiovascular mutation. Conclusion In summary, human myocardial tissue slices faithfully recapitulate doxorubicin-induced cardiotoxicity. Importantly, our data suggests the utility of employing genetic testing for known cardiovascular mutations as a valuable step before initiating doxorubicin treatment. Additionally, we demonstrate for the first time the in vitro cardioprotective potential of dexrazoxane in a human tissue model. These findings pave the way for using ex vivo myocardial tissue slices to evaluate cardiotoxicity and their value as a powerful tool for evaluating the efficacy and delivery approaches.

Mapping the Single-cell Differentiation Landscape of Osteosarcoma.

The genetic intratumoral heterogeneity observed in human osteosarcomas (OS) poses challenges for drug development and the study of cell fate, plasticity, and differentiation, processes linked to tumor grade, cell metastasis, and survival. To pinpoint errors in OS differentiation, we transcriptionally profiled 31,527 cells from a tissue-engineered model that directsMSCs toward adipogenic and osteoblastic fates. Incorporating pre-existing chondrocyte data, we applied trajectory analysis and non-negative matrix factorization (NMF) to generate the first human mesenchymal differentiation atlas. This 'roadmap' served as a reference to delineate the cellular composition of morphologically complex OS tumors and quantify each cell's lineage commitment. Projecting a bulk RNA-seq OS dataset onto this roadmap unveiled a correlation between a stem-like transcriptomic phenotype and poorer survival outcomes. Our study quantifies OS differentiation and lineage, a prerequisite to better understanding lineage-specific differentiation bottlenecks that might someday be targeted therapeutically.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization.

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

Investigation of Hydrocolloid Plant Polysaccharides as Potential Candidates to Mimic the Functions of MUC5B in Saliva

The successful substitution of complex physiological fluids, such as human saliva, remains a major challenge in drug development. Although there are a large number of saliva substitutes on the market, their efficacy is often inadequate due to short residence time in the mouth, unpleasant mouthfeel, or insufficient protection of the teeth. Therefore, systems need to be identified that mimic the functions of saliva, in particular the salivary mucin MUC5B and the unique physiological properties of saliva. To this end, plant extracts known to contain hydrocolloid polysaccharides and to have mucus-forming properties were studied to evaluate their suitability as saliva substitutes. The aqueous plant extracts of Calendula officinalis, Fucus sp. thalli, and lichenan from Lichen islandicus were examined for composition using a range of techniques, including GC-MS, NMR, SEC, assessment of pH, osmolality, buffering capacity, viscoelasticity, viscoelastic interactions with human saliva, hydrocolloid network formation, and in vitro cell adhesion. For this purpose, a physiologically adapted adhesive test was developed using human buccal epithelial cells. The results show that lichenan is the most promising candidate to mimic the properties of MUC5B. By adjusting the pH, osmolality, and buffering capacity with K2HPO4, it was shown that lichenan exhibited high cell adhesion, with a maximum detachment force that was comparable to that of unstimulated whole mouth saliva.

Identification of a membrane-associated element (MAE) in the C-terminal region of SARS-CoV-2 nsp6 that is essential for viral replication.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel coronavirus severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has rapidly spread worldwide since its emergence in late 2019. Its ongoing evolution poses challenges for antiviral drug development. Coronavirus nsp6, a multiple-spanning transmembrane protein, participates in the biogenesis of the viral replication complex, which accommodates the viral replication-transcription complex. The roles of its structural domains in viral replication are not well studied. Herein, we predicted the structure of the SARS-CoV-2 nsp6 protein using AlphaFold2 and identified a highly folded C-terminal region (nsp6C) downstream of the transmembrane helices. The enhanced green fluorescent protein (EGFP)-fused nsp6C was found to cluster in the cytoplasm and associate with membranes. Functional mapping identified a minimal membrane-associated element (MAE) as the region from amino acids 237 to 276 (LGV-KLL), which is mainly composed of the α-helix H1 and the α-helix H2; the latter exhibits characteristics of an amphipathic helix (AH). Mutagenesis studies and membrane flotation experiments demonstrate that AH-like H2 is required for MAE-mediated membrane association. This MAE was functionally conserved across MERS-CoV, HCoV-OC43, HCoV-229E, HCoV-HKU1, and HCoV-NL63, all capable of mediating membrane association. In a SARS-CoV-2 replicon system, mutagenesis studies of H2 and replacements of H1 and H2 with their homologous counterparts demonstrated requirements of residues on both sides of the H2 and properly paired H1-H2 for MAE-mediated membrane association and viral replication. Notably, mutations I266A and K274A significantly attenuated viral replication without dramatically affecting membrane association, suggesting a dual role of the MAE in viral replication: mediating membrane association as well as participating in protein-protein interactions.IMPORTANCESevere acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) assembles a double-membrane vesicle (DMV) by the viral non-structural proteins for viral replication. Understanding the mechanisms of the DMV assembly is of paramount importance for antiviral development. Nsp6, a multiple-spanning transmembrane protein, plays an important role in the DMV biogenesis. Herein, we predicted the nsp6 structure of SARS-CoV-2 and other human coronaviruses using AlphaFold2 and identified a putative membrane-associated element (MAE) in the highly conserved C-terminal regions of nsp6. Experimentally, we verified a functionally conserved minimal MAE composed of two α-helices, the H1, and the amphipathic helix-like H2. Mutagenesis studies confirmed the requirement of H2 for MAE-mediated membrane association and viral replication and demonstrated a dual role of the MAE in viral replication, by mediating membrane association and participating in residue-specific interactions. This functionally conserved MAE may serve as a novel anti-viral target.

Aptameric hirudins as selective and reversible EXosite-ACTive site (EXACT) inhibitors

Potent and selective inhibition of the structurally homologous proteases of coagulation poses challenges for drug development. Hematophagous organisms frequently accomplish this by fashioning peptide inhibitors combining exosite and active site binding motifs. Inspired by this biological strategy, we create several EXACT inhibitors targeting thrombin and factor Xa de novo by linking EXosite-binding aptamers with small molecule ACTive site inhibitors. The aptamer component within the EXACT inhibitor (1) synergizes with and enhances the potency of small-molecule active site inhibitors by many hundred-fold (2) can redirect an active site inhibitor’s selectivity towards a different protease, and (3) enable efficient reversal of inhibition by an antidote that disrupts bivalent binding. One EXACT inhibitor, HD22-7A-DAB, demonstrates extraordinary anticoagulation activity, exhibiting great potential as a potent, rapid onset anticoagulant to support cardiovascular surgeries. Using this generalizable molecular engineering strategy, selective, potent, and rapidly reversible EXACT inhibitors can be created against many enzymes through simple oligonucleotide conjugation for numerous research and therapeutic applications.

Enhancing effect of natural adjuvant, panduratin A, on antibacterial activity of colistin against multidrug-resistant Acinetobacter baumannii

Colistin- and carbapenem-resistant Acinetobacter baumannii is a serious multidrug resistant (MDR) bacterium in clinical settings. Discovery of new antibacterial drugs against MDR is facing multiple challenges in drug development. Combination of known antibiotics with a robust adjuvant might be an alternative effective strategy for MDR treatment. In the study herein, we report an antibiotic adjuvant activity of a natural compound panduratin A from fingerroot (Boesenbergia rotunda) as a potent adjuvant to colistin. The present study investigated the antibiotic adjuvant effect of panduratin A against 10 colistin- and carbapenem-resistant A. baumannii. Antibacterial activities were tested by broth microdilution method. Biofilm assay was used to determine the efficacy of panduratin A in biofilm formation inhibition on two representative strains Aci46 and Aci44. Genomic and transcriptomic analyses of colistin- and carbapenem-resistant A. baumannii strains were used to identify potential resistance and tolerance mechanism in the bacteria. Panduratin A-colistin combination showed an increased effect on antibacterial in the A. baumannii. However, panduratin A did not improve the antibacterial activity of imipenem. In addition, panduratin A improves anti-biofilm activity of colistin against Aci44 and Aci46, the colistin- and carbapenem-resistant A. baumannii. Panduratin A markedly enhances bactericidal and anti-biofilm activity of colistin against colistin- resistant A. baumannii. Based on genome comparisons, single nucleotide polymorphism (SNP) patterns in six genes encoding biofilm and lipid A biosynthesis were shared in Aci44 and Aci46. In Aci44, we identified a partial sequence of pmrB encoding a polymyxin resistant component PmrB, whereas a full length of pmrB was observed in Aci46. RNA-seq analyses of Aci44 revealed that panduratin A-colistin combination induced expression of ribosomal proteins and oxidative stress response proteins, whereas iron transporter and MFS-type transporter systems were suppressed. Panduratin A-colistin combination could promote intracellular reactive oxygen species (ROS) accumulation could lead to the cidal effect on colistin-resistant A. baumannii. Combination of panduratin A and colistin showed a significant increase in colistin efficacy against colistin- resistant A. baumannii in comparison of colistin alone. Genomic comparison between Aci44 and Aci46 showed mutations and SNPs that might affect different phenotypes. Additionally, based on RNA-Seq, panduratin A-colistin combination could lead to ROS production and accumulation. These findings confirmed the potency of panduratin as colistin adjuvant against multidrug resistant A. baumannii.

Predictive insights into plant-based compounds as fibroblast growth factor receptor 1 inhibitors: a combined molecular docking and dynamics simulation study

The discovery of novel therapeutic agents with potent anticancer activity remains a critical challenge in drug development. Natural products, particularly bioactive phytoconstituents derived from plants, have emerged as promising sources for anticancer drug discovery. In this study, we used virtual screening techniques to explore the potential of bioactive phytoconstituents as inhibitors of fibroblast growth factor receptor 1 (FGFR1), a key signaling protein implicated in cancer progression. We used virtual screening techniques to analyze phytoconstituents extracted from the IMPPAT 2.0 database. Our primary objective was to discover promising inhibitors of FGFR1. To ensure the selection of promising candidates, we initially filtered the molecules based on their physicochemical properties. Subsequently, we performed binding affinity calculations, PAINS, ADMET, and PASS filters to identify nontoxic and highly effective hits. Through this screening process, one phytocompound, namely Mundulone, emerged as a potential lead. This compound demonstrated an appreciable affinity for FGFR1 and exhibited specific interactions with the ATP-binding site residues. To gain further insights into the conformational dynamics of Mundulone and the reference FGFR1 inhibitor, Lenvatinib, we conducted time-evolution analyses employing 200 ns molecular dynamics simulations (MDS) and essential dynamics. These analyses provided valuable information regarding the dynamic behavior and stability of the compounds in complexes with FGFR1. Overall, the findings indicate that Mundulone exhibits promising binding affinity, specific interactions, and favorable drug profiles, making it a promising lead candidate. Further experimental analysis will be necessary to confirm its effectiveness and safety profiles for therapeutic advancement in the cancer field. Communicated by Ramaswamy H. Sarma

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities.

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Opportunities and challenges in drug development for retinal diseases

Opportunities and challenges in drug development for retinal diseases

Analgesic Peptides: From Natural Diversity to Rational Design.

Pain affects one-third of the global population and is a significant public health issue. The use of opioid drugs, which are the strongest painkillers, is associated with several side effects, such as tolerance, addiction, overdose, and even death. An increasing demand for novel, safer analgesic agents is a driving force for exploring natural sources of bioactive peptides with antinociceptive activity. Since the G protein-coupled receptors (GPCRs) play a crucial role in pain modulation, the discovery of new peptide ligands for GPCRs is a significant challenge for novel drug development. The aim of this review is to present peptides of human and animal origin with antinociceptive potential and to show the possibilities of their modification, as well as the design of novel structures. The study presents the current knowledge on structure-activity relationship in the design of peptide-based biomimetic compounds, the modification strategies directed at increasing the antinociceptive activity, and improvement of metabolic stability and pharmacodynamic profile. The procedures employed in prolonged drug delivery of emerging compounds are also discussed. The work summarizes the conditions leading to the development of potential morphine replacements.

Cytokeratin-18 is a sensitive biomarker of alanine transaminase increase in a placebo-controlled, randomized, crossover trial of therapeutic paracetamol dosing (PATH-BP biomarker substudy).

Drug-induced liver injury (DILI) is a challenge in clinical medicine and drug development. Highly sensitive novel biomarkers have been identified for detecting DILI following a paracetamol overdose. The study objective was to evaluate biomarker performance in a 14-day trial of therapeutic dose paracetamol. The PATH-BP trial was a double-blind, placebo-controlled, crossover study. Individuals (n = 110) were randomized to receive 1 g paracetamol 4× daily or matched placebo for 2 weeks followed by a 2-week washout before crossing over to the alternate treatment. Blood was collected on days 0 (baseline), 4, 7, and 14 in both arms. Alanine transaminase (ALT) activity was measured in all patients. MicroRNA-122 (miR-122), cytokeratin-18 (K18), and glutamate dehydrogenase (GLDH) were measured in patients who had an elevated ALT on paracetamol treatment (≥50% from baseline). ALT increased in 49 individuals (45%). All 3 biomarkers were increased at the time of peak ALT (K18 paracetamol arm: 18.9 ± 9.7 ng/ml, placebo arm: 11.1 ± 5.4 ng/ml, ROC-AUC = 0.80, 95% CI 0.71-0.89; miR-122: 15.1 ± 12.9fM V 4.9 ± 4.7fM, ROC-AUC = 0.83, 0.75-0.91; and GLDH: 24.6 ± 31.1U/l V 12.0 ± 11.8U/l, ROC-AUC = 0.66, 0.49-0.83). All biomarkers were correlated with ALT (K18 r = 0.68, miR-122 r = 0.67, GLDH r = 0.60). To assess sensitivity, biomarker performance was analyzed on the visit preceding peak ALT (mean 3 days earlier). K18 identified the subsequent ALT increase (K18 ROC-AUC = 0.70, 0.59-0.80; miR-122 ROC-AUC = 0.60, 0.49-0.72, ALT ROC-AUC = 0.59, 0.48-0.70; GLDH ROC-AUC = 0.70, 0.50-0.90). Variability was lowest for ALT and K18. In conclusion, K18 was more sensitive than ALT, miR-122, or GLDH and has potential significant utility in the early identification of DILI in trials and clinical practice.

Abstract 3537: Assessing machine learning models of drug response predictions using orthotopic patient-derived xenograft

Abstract Accurate prediction of drug response using machine learning (ML) remains a significant challenge in drug development as well as in personalized cancer therapy. Furthermore, there is a scarcity of rigorous external validation studies for evaluating drug response prediction models. Even when available, such validations are often confined to in-vitro studies. Orthotopic patient-derived xenograft (O-PDX) mice serve as essential preclinical models that closely replicate the human tumor microenvironment and assess the therapeutic response to cancer treatments. We present CertisAITM, a novel ensemble of machine learning models that have been trained on a vast array of experimental high-throughput screenings of both monotherapies and combination therapies, incorporating over 4500 investigational and FDA-approved drugs across 10 major cancer indications. The results of our ML drug response prediction models show an average R2 of 0.75 and RMSE of 0.62 (across internal 5-fold cross-validation) for all 10 cancer prediction models. Our evaluation of CertisAI's drug response predictions within O-PDX pharmacology studies has shown a notable overall correlation between actual observed tumor growth inhibition (TGI) and predicted TGI, with an r value of 0.45 across seven indications. The most accurate prediction model achieved an r value of 0.7 for colorectal cancer, encompassing 37 treatments in 6 O-PDX studies. Altogether, these results highlight CertisAI’s potential in enhancing pre-clinical model selection as well as personalized treatment strategies in oncology. Citation Format: Yuan-Hung Chien, Raffaella Pippa, Warren Andrews, Javier Rodriguez, Kristen Buck, Derrick Gorospe, Elizabeth Valencia, Bianca Carapia, Emily Eastwood, Jantzen Sperry, Jonathan Nakashima, Long Do. Assessing machine learning models of drug response predictions using orthotopic patient-derived xenograft [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 3537.

Abstract 5130: Phospho-proteomic dynamics reveal DNA damage and repair signatures of drug sensitivity

Abstract There are numerous drug development efforts of DNA damage response (DDR) targets, such as PARP, ATR, ATM, CHK1, where the ability to accurately identify responsive sub-populations for monotherapies and combinations is a major challenge. Patients are routinely assigned to these drugs based on BRCA mutation, or BRCAness profile, encompassing homologous recombination deficiencies (HRD) which do not always represent the responsive sub-populations. Since DNA replication and DNA repair mechanisms are dominated by signaling events, we hypothesized that HRD extends beyond the genomic status, and is manifested also in the level of protein expression and protein modification. To that end, we created a harmonized data atlas consisting of phenotypic drug sensitivity and basal and dynamic phosphoproteomics of multiple cell lines before and after treatment of multiple DDR drugs. Proteogenomic analysis of cell lines showed that some cell lines are sensitive to DDR drugs despite being DDR-proficient at the genomic level. Proteomic analysis of these cell lines showed that at least a third of them exhibit proteomic DDR deficiency, explaining DDR sensitivity beyond mutation status. We then utilized our proteogenomic atlas and computational platform to address challenging clinical needs, namely discovery of a predictive ATR biomarker and identifying potential combination treatments to overcome ATRi resistance. Using basal state and protein dynamics of ATR coupled with ATRi sensitivity, we developed a novel predictive ATR biomarker, and tested it using two independent validation datasets. Our ATR biomarker exhibited improved precision and recall compared to genomic biomarkers represented by mutations commonly used in the clinic, and was not confounded by mutations. In addition, integrated analysis of basal and dynamic data of multiple cell lines treated with ATRi, highlighted both known (e.g. ATM) and novel resistance mechanisms and suggested potential drug combinations. Altogether, we demonstrate here the benefit of utilizing (phospho)-proteomic data to tackle clinical challenges in DDR drug development, such as predictive biomarkers and combination strategies. We provide a proteomics-based framework that would enable future patient selection in DDR clinical trials. Citation Format: Gali Arad, Avital Hay-Koren, Nitzan Simchi, Dimitri Kovalerchik, Dina Daitchman, Amit Manor, Ofer Givton, Elyad Lezmi, Iris Alchanati, Yonatan Katzenelenbogen, Shay Herman, Alon Shtrikman, Galina Otonin, Eran Seger, Kirill Pevzner. Phospho-proteomic dynamics reveal DNA damage and repair signatures of drug sensitivity [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 5130.

Abstract 3757: The development of MAGMAS as a potential therapeutic target in temozolomide-resistant glioma

Abstract Glioblastoma (GBM) is one of the most aggressive types of brain cancer and remains a significant challenge for drug development in oncology today. Temozolomide (TMZ) is the standard of care for GBM, and resistance to this drug is modulated partly by the DNA repair enzyme O6-methylguanine-DNA methyltransferase (MGMT). Clinical studies demonstrated that low MGMT expression in GBM enhances sensitivity to TMZ and prolongs survival for patients. However, recent clinical studies indicate MGMT is a discordant biomarker since the predictive value of MGMT methylation is relatively low and there is a lack of modalities to inhibit MGMT expression. Furthermore, glioma stem cells (GSC) are another contributing factor for resistance due to tumor heterogeneity, self-renewal capacity and activation of DNA repair responses to overcome the effect of radiation and TMZ treatment. These resistance mechanisms contribute to tumor recurrence and present major impediments for the development of effective treatments. MAGMAS, 13.8 kDa mitochondria protein, is a translocase of the inner mitochondrial membrane and regulates oxidative phosphorylation. Specifically, MAGMAS regulates the ATP stimulatory activity of DNAJC19 on mtHSP70 that drives the movement of peptides through the TIM23 complex into the mitochondrial matrix. Recent evidence suggests that MAGMAS can regulate oxidative phosphorylation via interactions with the respiratory complexes present in the inner mitochondrial membrane. Previously, our lab has shown MAGMAS overexpression in malignant glioma tissues of patients and murine intracranial xenografts. Additionally, pharmacological inhibition of MAGMAS with the small molecule BT9 promoted cytotoxicity and impaired respiratory functions in malignant glioma cells demonstrating that MAGMAS is a promising potential target for GBM patients. However, further studies are needed to investigate the role of MAGMAS across the spectrum from initial tumorigenesis to disease progression and to further determine how MAGMAS is implicated during the development of treatment resistance. This study is designed to investigate the role of MAGMAS in GSCs and their contribution to TMZ resistance. We observed temozolomide-resistant (TR) U251-TR and D54 MG-TR GBM cell lines expressed significantly higher levels of MAGMAS and several stem cell markers than their drug-sensitive (S) parental cell lines. Therefore, we hypothesized that silencing MAGMAS re-sensitizes TR GBM cell lines to TMZ. We found that MAGMAS knockdown in D54-MG and U251 S/TR cells in the presence of escalating TMZ concentrations, caused enhanced sensitivity to TMZ. Consistent with these results, inhibition of MAGMAS with the novel BT9 inhibitor enhances the TMZ sensitivity in the TMZ-resistant cell lines. Additional studies of patient-derived cell lines are being utilized further to validate MAGMAS as it is an attractive target in TMZ-resistant GBM patients. Citation Format: Jennifer D. Tran, Javier J. Lepe, Maria C. Kenney, Bhaskar C. Das, Daniela A. Bota. The development of MAGMAS as a potential therapeutic target in temozolomide-resistant glioma [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 3757.

Abstract 4079: EGL-001 is a novel immunocytokine designed to specifically target and disarm T regulatory cells in the tumor microenvironment

Abstract Introduction Immune checkpoint blockade (ICB) therapies targeting PD-1/PD-L1 or CTLA-4 have greatly improved clinical outcome in many cancers. However, multiple mechanisms of resistance to ICB severely limit the number of patients experiencing long-term survival benefits. Overcoming resistance to ICB represents a significant challenge for drug development. Regulatory T cells (Tregs) in the tumor microenvironment (TME) prevent effector T cells (Teffs) from mounting a productive immune response and have been implicated in ICB resistance. Novel therapeutic strategies aimed at disarming Tregs in the TME could restore anti-tumor responses and sensitize patients to ICB therapy. Treg survival is dependent on interleukin-2 (IL-2), which signals in Tregs via the high affinity IL-2 receptor (IL-2R). Tumor-associated Tregs differentiate from tumor-associated Teffs and from peripheral Tregs by higher expression of CD25, the IL-2R alpha chain. Tregs in the TME also express higher surface levels of CTLA-4 as compared to peripheral Tregs. Thus, CD25 and CTLA-4 overexpression could allow preferential targeting of tumor-associated Tregs. Methods and Results We used single-cell RNA sequencing data from cancer patients and flow cytometry analysis of human tumor samples to verify that CD25 and CTLA-4 are selectively overexpressed in tumor-associated Tregs as compared with peripheral Tregs or other T cells. Based on these findings, we developed EGL-001, a novel immunocytokine (ICK) comprising a humanized IgG1 directed against CTLA-4 fused to a Treg-specific IL-2R antagonist IL-2 mutein, to selectively target tumor infiltrating Tregs. EGL-001 IgG1 moiety binds membrane CTLA-4, as demonstrated by flow cytometry, and competes with CTLA-4-CD80/86 interaction, as evaluated by ELISA. EGL-001 IL-2 mutein moiety binds to CD25, as demonstrated by bio-layer interferometry, and specifically blocks high-affinity IL-2R signaling in Tregs. Signaling blockade is further potentiated by a CTLA-4 dependent CD25 downmodulation process evidenced by confocal microscopy and flow cytometry. We demonstrate that EGL-001 unique tumor-associated Treg-targeted dual mechanism of action results in increased Treg apoptosis and decreased Treg frequencies in multiple in vitro, ex vivo and in vivo systems. Importantly, EGL-001 only minimally affects IL-2 signaling and survival of Teffs, which are then expected to be unleashed from Treg cell-mediated suppression. Accordingly, EGL-001 induces anti-tumor responses in mice in combination with anti-PD1 therapy. Conclusions EGL-001 is an ICK with a novel and unique mechanism of action combining ICB and Treg selective IL-2 starvation, designed to specifically disarm and deplete tumor-associated Tregs. Based on our preclinical data, EGL-001 is being developed as a cancer immunotherapeutic to improve patients’ response to ICB and counteract ICB resistance. Citation Format: Fiorella Kotsias, Caroline Janot-Sardet, Pamela Pamela Caudana, Erica Russo, Marine Dubois, Andres Zucchetti, Mercedes Tkach, Sebastien Lemoine, Ilgar Z. Mamedov, Alvaro Lladser, Jordan Denizeau, Annalisa Meola, Christine Sedlik, Edith Borcoman, Felix Rey, Pablo Guardado-Calvo, Bernard Vanhove, Monica Gostissa, Eliane PIaggio. EGL-001 is a novel immunocytokine designed to specifically target and disarm T regulatory cells in the tumor microenvironment [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 4079.

CHARACTERISATION, EVALUATION AND DENSITY FUNCTIONAL ANALYSIS OF CILNIDIPINE-NICOTINAMIDE COCRYSTALS DEVELOPED BY LIQUID ASSISTED GRINDING TECHNIQUE: A SUSTAINABLE APPROACH FOR ENHANCED SOLUBILITY

Objective: Improving the solubility of poorly water-soluble drugs has always been a challenge in drug development. This study aimed to enhance the aqueous solubility of a poorly water-soluble drug, Cilnidipine, by cocrystallisation method using liquid-assisted grinding (LAG) technique with Nicotinamide as the coformer. The study also aimed to understand the mechanism of cocrystal formation by quantum mechanical calculations. Methods: The Cilnidipine-Nicotinamide cocrystals were prepared in various stoichiometric ratios using the liquid-assisted grinding (LAG) technique. The cocrystals obtained were characterised by vibrational spectroscopy, thermal methods such as differential scanning calorimetry (DSC), powder X-ray diffraction (PXRD) and surface morphology by field emission scanning electron microscopy (FE-SEM). The cocrystals were evaluated for saturation solubility, and the mechanistic study of cocrystal formation was performed using the Gaussian 09 software package. Results: FT-IR spectra of the formulated cocrystal indicated the intermolecular hydrogen bond formation between-N-H of Nicotinamide and the nitro group of Cilnidipine. DSC analysis showed a single endotherm at 96.76 °C, PXRD patterns were different from that of the reactants, and FE-SEM analysis revealed the changes in the surface morphology of the obtained cocrystal. The prepared cocrystal showed a 26.36-fold enhancement in the aqueous solubility of Cilnidipine. The DFT study demonstrated the formation of a strong intermolecular hydrogen bonding between the nitro-oxygen atom of Cilnidipine and the amide hydrogen atom of Nicotinamide in cocrystal formed. Conclusion: This study highlights the potential of the liquid-assisted grinding method for preparing cocrystals as a sustainable and reliable approach to address the challenges posed by poorly water-soluble drugs.

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease.

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB) permeability is predicted and the anti-inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean-DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂-induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti-neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.

A review on solubility enhancement technique for pharmaceutical drugs

Solubility, a critical parameter governing the bioavailability and therapeutic efficacy of pharmaceutical compounds, often poses a significant challenge in drug development. This review article offers a thorough examination of several methods used to improve the solubility of medications that have low solubility, in order to overcome their limits. The discussion encompasses both conventional and emerging strategies, highlighting their mechanisms, advantages, and limitations. The study examines conventional methods such as particle size reduction, solid dispersion, and cosolvency, focusing on their historical importance and extensive use. Advances in nanotechnology, including nanosuspensions, nanocrystals, and lipid-based nanocarriers, are discussed for their potential to revolutionize solubility enhancement through improved drug delivery systems. An essential determinant in achieving optimal medication dosage absorption into the circulatory system is the demonstration of a pharmacological action is directly linked to the solubility of a substance. The primary challenge in formulating the new medicinal chemical is its limited water solubility. Medications with low solubility in water need large doses in order to achieve their highest effective concentration in the bloodstream when taken orally. The biopharmaceutical categorization system (BCS) classifies substances according to their solubility and permeability. Regulatory agencies and health organizations have used this categorization approach to validate bioequivalence for chemicals that are both highly soluble and very permeable by using dissolution as a form of confirmation. Medications that have a poor capacity to dissolve in water have a slow pace of breaking down, which results in a reduction in the amount of the medication that may be absorbed into the bloodstream when taken orally.

Computational drug development for membrane protein targets.

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.