Clinical Drug Research Articles

Chitosan nanoparticles improve the effectivity of miltefosine against Acanthamoeba.

Acanthamoeba keratitis (AK) is a corneal sight-threatening infection caused by the free-living amoebae of the genus Acanthamoeba. Early and appropriate treatment significantly impacts visual outcomes. Mucoadhesive polymers such as chitosan are a potential strategy to prolong the residence time and bioavailability of the encapsulated drugs in the cornea. Regarding the recent administration of miltefosine (MF) for treating resistant AK, in the present study, we synthesized miltefosine-loaded chitosan nanoparticles (MF-CS-NPs) and evaluated them against Acanthamoeba. Chitosan nanoparticles (CNPs) were prepared using the ionic gelation method with negatively charged tripolyphosphate (TPP). The zeta-potential (ZP) and the particle size of MF-CS-NPs were 21.8±3.2 mV and 46.61±18.16 nm, respectively. The release profile of MF-CS-NPs indicated linearity with sustained drug release. The cytotoxicity of MF-CS-NPs on the Vero cell line was 2.67 and 1.64 times lower than free MF at 24 and 48 hours. This formulation exhibited no hemolytic activity in vitro and ocular irritation in rabbit eyes. The IC50 of MF-CS-NPs showed a significant reduction by 2.06 and 1.69-fold in trophozoites at 24 and 48 hours compared to free MF. Also, the MF-CS-NPs IC50 in the cysts form was slightly decreased by 1.26 and 1.21-fold at 24 and 48 hours compared to free MF. The MF-CS-NPs were more effective against the trophozoites and cysts than free MF. The nano-chitosan formulation was more effective on trophozoites than the cysts form. MF-CS-NPs reduced toxicity and improved the amoebicidal effect of MF. Nano-chitosan could be an ideal carrier that decreases the cytotoxicity of miltefosine. Further analysis in animal settings is needed to evaluate this nano-formulation for clinical ocular drug delivery.

Malate, a natural inhibitor of 6PGD, improves the efficacy of chemotherapy in lung cancer

ObjectiveMetabolic reprogramming is an important coordinator of tumor development and resistance to therapy, such as the tendency of tumor cells to utilize glycolytic energy rather than oxidative phosphorylation, even under conditions of sufficient oxygen. Therefore, targeting metabolic enzymes is an effective strategy to overcome therapeutic resistance. Materials and MethodsWe explored the differential expression and growth-promoting function of MDH2 by immunohistochemistry and immunoblotting experiments in lung cancer patients and lung cancer cells. Pentose phosphate pathway-related phenotypes (including ROS levels, NADPH levels, and DNA synthesis) were detected intracellularly, and the interaction of malate and proteinase 6PGD was detected in vitro. In vivo experiments using implanted xenograft mouse models to explore the growth inhibitory effect and pro-chemotherapeutic function of dimethyl malate (DMM) on lung cancer. ResultsWe found that the expression of malate dehydrogenase (MDH2) in the tricarboxylic acid cycle (TCA cycle) was increased in lung cancer. Biological function enrichment analysis revealed that MDH2 not only promoted oxidative phosphorylation, but also promoted the pentose phosphate pathway (PPP pathway). Mechanistically, it was found that malate, the substrate of MDH2, can bind to the PPP pathway metabolic enzyme 6PGD, inhibit its activity, reduce the generation of NADPH, and block DNA synthesis. More importantly, DMM can improve the sensitivity of lung cancer to the clinical drug cisplatin. ConclusionWe have identified malate as a natural inhibitor of 6PGD, which will provide new leads for the development of 6PGD inhibitors. In addition, the metabolic enzyme MDH2 and the metabolite malate may provide a backup option for cells to inhibit their own carcinogenesis, as the accumulated malate targets 6PGD to block the PPP pathway and inhibit cell cycle progression.

Development of a Probabilistic Boolean network (PBN) to model intraoperative blood pressure management

BackgroundBlood pressure is a vital sign for organ perfusion that anesthesiologists measure and modulate during surgery. However, current decision-making processes rely heavily on clinicians’ experience, which can lead to variability in treatment across surgeries. With the advent of machine learning, we can now create models to predict the outcomes of interventions and guide perioperative decision-making. The first step in this process involves translating the clinical decision-making process into a framework understood by an algorithm. Probabilistic Boolean networks (PBNs) provide an information-rich approach to this problem. A PBN trends toward a steady state, and its decisions are easily understood via its Boolean predictor functions. We hypothesize that a PBN can be developed that corrects hemodynamic instability in patients by selecting clinical interventions to maintain blood pressure within a given range. MethodsData on patients over the age of 65 undergoing surgery with general anesthesia from 2018 to 2020 were drawn from the UF Health PRECEDE data set with IRB approval (IRB201700747). Parameters examined included heart rate, blood pressure, and frequency of medications given 15 min after anesthetic induction and 15 min before awakening. The medication frequency data were truncated into a 66/33 split for the training and validation set used in the PBN. The model was coded using Python 3 and evaluated by comparing the frequency of medications chosen by the program to the values in the testing set via linear regression analysis. ResultsThe network developed successfully models a hemodynamically unstable patient and corrects the imbalance by administering medications. This is evidenced by the model achieving a stable, steady state matrix in all iterations. However, the model's ability to emulate clinical drug selection was variable. It was successful with its use of vasodilator selection but struggled with the appropriate selection of vasopressors. ConclusionsThe PBN has demonstrated the ability to choose appropriate interventions based on a patient's current vitals. Additional work must be done to have the network emulate the frequency at which drugs are selected from in clinical practice. In its current state, the model provides an understanding of how a PBN behaves in the context of correcting hemodynamic instability and can aid in developing more robust models in the future.

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 6895: Application of MiniPDX® in clinical indication identification and anti-tumor drug development

Abstract MiniPDX® (Mini-patient-derived xenograft) is a novel, rapid (7~10days), and accurate method for drug sensitivity testing in vivo. It was referred as in vivo version of organoid assy. The testing material can be fresh patient tumor samples or tissues from established PDX models. We have systematically evaluated and compared the response rates of MiniPDX® assays and PDX assays pairwise in 26 PDX models across 3 types of cancers to 12 clinically relevant regimens for chemical and targeting drugs. The results demonstrate a high correlation between drug responses of the two assays, with sensitivity of 80% and specificity of 93%. More and more studies showed that MiniPDX® drug sensitivity test results were consistent with clinical responses in most patients, which indicated MiniPDX® models have great potential in guiding personalized medicine. LIDE has successfully conducted over 3,000 MiniPDX® tests for clinical precision medicine, covering more than 50 indications. The application of MiniPDX® extends beyond therapeutic selection, facilitating the prioritization of approved pharmacological interventions and informing the discovery pipeline for novel agents. Importantly, the MiniPDX® Mouse Trial using fresh tumor samples generated from the clinic is beneficial for determination of potential clinical indications. Only thousands of fresh cells leftover from MiniPDX® preparation are sufficient to get genomic and transcription data by OncoVee ® K-cell technology. The combination of MiniPDX® assay and omics data would be very useful for determination of potential biomarkers to distinguish responders and non-responders in population with certain indication(s) and leveraged for patient stratification and selection criteria in clinical trial design. In addition, we have developed a new version of MiniPDX® assay for immunotherapy (IO-FIVE, Immuno-Oncology Fast In Vivo Efficacy test) by using fresh patient cancer cells with autologous immune cells to maximally mimic human tumor microenvironment before seeding in immune-deficient mice. This assay allows for the tracking of cellular viability and phenotype pre- and post-treatment, thus providing a robust metric of in vivo immunotherapeutic efficacy within a fortnight. Citation Format: Bin Xie, Hongkui Chen, Shizhu Zhao, Le Li, Song Xi, Yu Di, Yang Liu, Xinlei Chen, Xiaorong Gu, Zhenle Bi, Kaimeng Hu, Loc Van, Josh Caggiula, Candice Tang, Danyi Wen. Application of MiniPDX® in clinical indication identification and anti-tumor drug development [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 6895.

Abstract 5864: YAP1 defines a highly plastic, persister cell population in small cell lung cancer (SCLC) following relapse

Abstract SCLC is an aggressive, neuroendocrine malignancy notable for rapid, albeit transient, responses to therapy. We have previously shown that transcriptional heterogeneity among treatment-naïve SCLC tumors underlies four distinct transcriptional subtypes, each with distinct clinical vulnerabilities. Though previously hypothesized to delineate a distinct subtype, expression of YAP1 is largely absent from treatment-naïve SCLC. Less clearly defined is the transcriptional landscape of relapsed SCLC, the evolution of which may underlie SCLC’s capacity for insurmountable resistance. To better define the biology of relapsed SCLC, we performed mass cytometry analysis of patient circulating tumor cells (CTCs), immunohistochemistry for ASCL1, NEUROD1, POU2F3, MYC and YAP1, and single-cell (sc)RNAseq of core needle biopsies from SCLC patients and preclinical models following resistance to standard-of-care therapies. In contrast to treatment-naïve SCLC, YAP1 mRNA and protein are detectable in patient CTC populations and tumors from relapsed patients and PDXs, albeit at levels lower than subtype-defining transcription factors (e.g. ASCL1, NEUROD1, or POU2F3). Notably, however, scRNAseq reveals these YAP1 expressing cells to be mutually exclusive with ASCL1 and NEUROD1, though not necessarily POU2F3. YAP1-expressing cells possess unique mesenchymal, inflamed features and exhibit high levels of transcriptional plasticity. Consistent with the upregulation of YAP1 following chemotherapy, YAP1-positive cell populations express senescence associated secretory phenotype genes and cancer stem cell genes, suggesting that these populations represent drug tolerant persister cells that may serve as a source for renewal of classical neuroendocrine populations within the tumor. Targeting these resistant populations may be critical to re-establish response in relapsed SCLC, and these analyses identified high expression of several surface proteins that are currently under investigation as targets for antibody-drug conjugates in SCLC clinical trials, including B7H3, TROP2 and HER2 antibody drug conjugates. These data suggest that the acquisition of YAP1 expression delineates a particularly recalcitrant tumor cell population observed almost exclusively in relapsed SCLC that is capable of driving tumor persistence and treatment resistance. Clinically, these data underscore the importance of constraining transcriptional plasticity in the frontline management of SCLC, but also of developing therapies aimed specifically at emergent populations capable of driving resistance, as with the YAP1-high cells identified here. The emergence of YAP1 following resistance in SCLC and the observation of YAP1 as a common feature of other high-grade neuroendocrine carcinomas exclusive of SCLC, likely explains the initial mischaracterization of YAP1 as a subtype-defining feature of SCLC. Citation Format: C. Allison Stewart, Sarah M. Groves, Runsheng Wang, Kavya Ramkumar, Yuanxin Xi, Lixia Diao, Alejandra G. Serrano, Azusa Tanimoto, Ashley M. Victorian, Sayantan Bhattacharyya, Alexa J. Halliday, Neda Kalhor, Luisa Maren Soto, Pedro Filipe Da Rocha, Natalie Vokes, Loukia G. Karacosta, Jing Wang, John V. Heymach, Vito Quaranta, Bonnie Glisson, Lauren Averett Byers, Carl M. Gay. YAP1 defines a highly plastic, persister cell population in small cell lung cancer (SCLC) following relapse [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 5864.

Abstract 2551: Harness the power of real-world-data for oncology target and biomarker research: A case study by applying clinic-genomics data for 2,659 lung cancer patients and beyond

Abstract Randomized controlled trials (RCTs) are the gold standard for evaluating drug safety and efficacy in controlled settings, but they have limited generalizability due to their focus on selective and homogeneous populations. Real-world-data (RWD), collected from larger and more varied patient populations, can address inherent limitations of RCTs and aid in clinical trial design and drug approval. However, with the challenges of RWD, unlocking its full potential for tumor target and biomarker identification and validation demands innovative analytical approaches. Here we present RWD analyses of ConcertAI® electronic health records (EHR) linked with Caris Lab data for 2,659 lung cancer patients. Comprehensive genomics analyses incorporate WES and RNA-seq data, coupled with rich clinical EHR data. By integrating clinical and genomics data, we showcase the potential of harnessing RWD as follows: (1) we investigated genomic alterations across five histology groups (adenocarcinoma, squamous, large cell, small cell lung cancer [SCLC], neuroendocrine [NE] lung cancer). Notably, TP53 and LRP1B mutations were prevalent across all groups. LRP1B mutations correlated with higher tumor mutation burden, indicative of a favorable response to immunotherapy. Biallelic TP53 and RB1 inactivation was predominant in SCLC and NE lung tumors. The five histological groups exhibit distinct expression profiles, reflecting tumor intrinsic characteristics. Most immune gene signature scores were significantly lower in SCLC and NE tumors, aligned with that they are cold tumors with immune resistance, except CD56-centered NK and neuroendocrine gene signatures; (2) we analyzed patient treatment patterns and persistence by line of therapy. Among the 206 NSCLC patients with clinically actionable EGFR mutations, 143 received targeted therapy during their treatment courses (69.4%). Time to treatment discontinuation (TTD) was used as a surrogate clinical outcome endpoint. TTD was significantly longer when TKI was the first line treatment compared to later lines (p = 0.01), while the trend is opposite for ICI where TTD is significantly shorter in the 1st line setting (p = 0.025); (3) we highlight the unique potential of leveraging H&E imaging modality in such RWD. In summary, we successfully identified unique molecular signatures among various lung cancer histological groups, pinpointed distinct subpopulations within lung adenocarcinoma and squamous tumors linked to their immune phenotypes, and investigated the different patient treatment patterns and outcomes, facilitating personalized medicine. In the broader context of our work, we have developed a framework for harnessing various data modalities (clinical, multi-omics, imaging) within AbbVie's oncology RWD realm, enhancing the discovery and validation of new oncology targets and biomarkers. Citation Format: Si Wu, Weilong Zhao, Christy Choi, Mona Cai, Peter Ansell, Alexander Liede, Rong Chen, Xi Zhao, Josue Samayoa. Harness the power of real-world-data for oncology target and biomarker research: A case study by applying clinic-genomics data for 2,659 lung cancer patients and beyond [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 2551.

Abstract 5990: Microtubule-associated serine/threonine kinase-likeinhibitor, AD1208, as a novel cancer therapeutics

Abstract Microtubule-associated serine/threonine kinase-like is an essential regulator of mitosis and emerged as a novel oncogenic kinase. It is upregulated in several cancer types, correlating with chromosome instability and poor patient survival. Here, we are reporting a MASTL inhibitor, AD1208, as a new cancer therapeutics.AD1208 inhibits kinase activity of MASTL with IC50 value of 7nM in competitive manner against ATP and induces apoptosis of colorectal cancer cells with IC50 of 150nM. Selectivity index is higher than 100 folds when the cytotoxicity was measured in normal colon cells. Synthetic lethality with BRCA was observed in knock out studies. Cell panel analysis, consist of 300 cell lines, indicated that the AD1208 is effective in cancers originated from stomach, colon, leukemia, lymphoma, and soft tissue. In vivo efficacy was confirmed in mouse xenograft model in dose dependent manner and synergistic effect with PARP inhibitor was observed. Kinase Assay Panel screening revealed that AD1208 is highly selective against 468 kinases. Inhibition of MASTL resulted in decreasing phosphorylated ENSA, inactivating CDK1 and arresting cell cycle progression at G2/M phase. Clinical adverse drug reactions of AD1208 were predicted by in vitro Safety Functional Panel screening to exhibit no meaningful safety-related issues. Currently, AD1208 is being conducted in IND enabling GLP-Tox studies, including genotoxicity, mutagenicity, safety pharmacology as well as in vivo safety studies. Taken together, AD1208 is identified as a novel and selective MASTL inhibitor exhibiting significant therapeutic potential to be a development candidate. Citation Format: SOONGYU CHOI, JS Ahn, KS Baek, YL Choi, MY Im, JH Kim, JW Kim, JH Khoo, SH Lee, WL Yao, Young-Whan Park. Microtubule-associated serine/threonine kinase-likeinhibitor, AD1208, as a novel cancer therapeutics [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 5990.

Pediatric Formulations Developed by Extrusion-Based 3D Printing: From Past Discoveries to Future Prospects.

Three-dimensional printing (3DP) technology in pharmaceutical areas is leading to a significant change in controlled drug delivery and pharmaceutical product development. Pharmaceutical industries and academics are becoming increasingly interested in this innovative technology due to its inherent inexpensiveness and rapid prototyping. The 3DP process could be established in the pharmaceutical industry to replace conventional large-scale manufacturing processes, particularly useful for personalizing pediatric drugs. For instance, shape, size, dosage, drug release and multi-drug combinations can be tailored according to the patient's needs. Pediatric drug development has a significant global impact due to the growing needs for accessible age-appropriate pediatric medicines and for acceptable drug products to ensure adherence to the prescribed treatment. Three-dimensional printing offers several significant advantages for clinical pharmaceutical drug development, such as the ability to personalize medicines, speed up drug manufacturing timelines and provide on-demand drugs in hospitals and pharmacies. The aim of this article is to highlight the benefits of extrusion-based 3D printing technology. The future potential of 3DP in pharmaceuticals has been widely shown in the last few years. This article summarizes the discoveries about pediatric pharmaceutical formulations which have been developed with extrusion-based technologies.

Abstract 5802: An orally available small molecule JMBI-001 elicits MYC-synthetic lethality and anti-tumor immunity by disabling MKLP2-mediated cellular processes

Abstract The MYC oncogene, a pivotal regulator of various cellular processes, is deregulated in approximately 70 % of human malignancies. Mitotic Kinesin-like Protein 2 (MKLP2) plays a versatile role in both interphase and mitosis and has emerged as a significant prognostic indicator and therapeutic target in cancer. The undruggability of MYC and the scarcity of MKLP2 inhibitors, however, have impeded clinical translation. Our development of JMBI-001, a potent and orally bioavailable small-molecule compound, overcomes these barriers. JMBI-001 elicits loss of function phenotypes in MKLP2 and a synthetic lethal interaction with MYC overexpression. Extensive kinome and safety profiling have revealed no significant off-target effects, and the compound is well-tolerated in long-term animal studies. In preclinical models, JMBI-001 has demonstrated an average tumor growth inhibition rate of 75 % across more than 20 MYC-overexpressing tumor models, including those in the stomach, lung, colon, liver, breast, kidney, skin, and hematopoietic system. Its anti-tumor activity positively correlates with high MYC abundance, aligning with the selective eradication of cells with abundant MYC both in vitro and in vivo. Notably, JMBI-001 also robustly stimulates systemic anti-tumor immunity, enhancing NK and CD3+ T cell infiltration in tumors of syngeneic cancer models. Moreover, additive or synergistic effects have been observed when combined with anti-PD1 therapy even in tumors refractory to the immune checkpoint blockade (ICB) therapy. The dual therapeutic actions of JMBI-001 stem from its disruption of MKLP2 functionalities, leading to anomalies, such as Golgi fragmentation in interphase and multipolarity in pro-metaphase. These disruptions lead to two key outcomes: apoptosis and immunogenic cell death, marked by secreted ATP, released high mobility group protein B1 (HMGB1), and surface-exposed calreticulin. These abnormalities, primed and amplified by deregulated MYC, are not observed in non-transformed cells, suggesting their potential as pharmacodynamic markers for monitoring JMBI-001's activity in vivo. In conclusion, JMBI-001 represents a novel class of anticancer agents that simultaneously triggers MYC synthetic lethality and anticancer immunity by targeting MKLP2-mediated cellular processes. Its unique mechanisms of action, exceptional bioavailability, potency at low nanomolar concentrations, wide-spectrum efficacy, and favorable safety profile establish JMBI-001 as a promising clinical study-ready drug candidate for treating MYC-driven cancers. Citation Format: Ting Zhang, Qiong Shi, Julia Kalashova, Xumei Liu, Xiaohu Zhou, Chenglu Yang, Yan Long, Hongmei Li, Jinhua Li, Gang Lv, Duo Yu, Xuejiao Jiang, Shenqiu Zhang, Jing Zhang, Hong Liu, Dun Yang. An orally available small molecule JMBI-001 elicits MYC-synthetic lethality and anti-tumor immunity by disabling MKLP2-mediated cellular processes [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 5802.

Abstract 7178: Neuronal cells derived from iPSCs cell to evaluate neurotoxicity after 48 or 72 hours in high-through put screening format

Abstract Neurotoxicity is a major concern in central nervous system drug discovery and a frequent cause of attrition in clinical trials or approved drug withdrawal. Neuropathy is a side effect defined by the injury of peripheral nerves causing a loss of sensation/motion. Drug development pipelines typically involve testing in cell lines followed by animal investigations with translation to humans. The induced pluripotent stem cells (iPSCs) have widened new model systems to study adverse toxicities. With evolution in regulatory guidance the use of human- iPSC-derived tissue provides high-throughput access and a more relevant testing environment. Neuropathy is one of the most chemotherapy adverse effects. In this study we have screened 8 compounds. Bortezomib (proteasome inhibitor), gemcitabine (approved ovarian cancer treatment), ivermectin (parasitic diseases treatment), flavopiridol (approved CDKs inhibitors), SNS-032 (approved CDKs inhibitor), mefloquine (anti-malaria drug), digitonin (nonionic detergent) and tamoxifen (hormone receptor-positive breast cancer treatment). These molecule toxicities were tested on neuronal-derived iPSCs Dopaneurone, GABAneurone, Glutaneurone, Motorneurone, Microglya and Astrocytes in high throughput screening. For the project, cells were plated in 384 well/plates. DMSO tolerance was performed (up to 10%). Drugs were added, then, after 48h or 72h, total protease and protease activities were measured using CytoTox-GloTM. Compounds were tested at 7 concentrations (from 10−4 to 10−9M).In this study we have validated cell culture and the assay conditions. The signal window range was good after 48h and 72h of treatment. No toxicity was found until 10% DMSO. A specific neurotoxicity signature was measurable for each drug tested. Digitonin, our positive control, induced toxicity in all IPSCs after 48h and 72h of incubation. On the contrary, Tamoxifen, our negative control, did not induce any neurotoxicity. The other molecules generated a signature of severe to mild neurotoxicity. These results are in agreement with their known adverse side-effects. In conclusion, this panel is a good tool to anticipate possible neurotoxicity within the 3Rs respect. It can be used in early drug de-risking or neuroprotection screening, with the aim of preventing/reducing/curing neuropathy in at-risk populations. Citation Format: Fabienne Sabatier, Justine Chaigne, Maryse Martin, Rodolphe Barbeau, Luisa Houssin, Stephane Bonnin, Alastair King, Vanessa Jahnke. Neuronal cells derived from iPSCs cell to evaluate neurotoxicity after 48 or 72 hours in high-through put screening format [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 7178.

Abstract 4884: Evaluating the utility of in silico variant annotation tools for cancer driver detection

Abstract In cancer genomics, precise variant annotation is crucial for clinical decisions, drug development, and research. The burgeoning genomic data offers an opportunity to use data-driven approaches to generate knowledge that supports clinical decisions. Particularly machine learning (ML) and Deep learning (DL), are becoming essential, as their application is fast, scalable, simple to implement, and generates reproducible results. The methods ranging from simple sequence-based alignment scoring to advanced algorithms like Logistic regression, Support vector machine, and Recurrent neural networks, have been employed by multiple variant annotation tools. This study compares in-silico methods available in Ensembl's Variant Effect Predictor (VEP) using a test dataset with COSMIC annotations. ML/DL success relies on robust training sets with comprehensive genomic variants features, including effects on transcription/translation, genomic context, annotation resources, in silico pathogenicity predictions, and population allele frequency. The training set, composed of known benign or pathogenic variants, serves as a reference for these algorithms to classify new and unseen variants. Our analysis reveals a limited concordance between the prediction algorithms. Despite comparable true/false positives/negatives, discrepancies persist in variant classification. Certain algorithms exhibit a propensity to over or under-call deleterious mutations. Some demonstrate a tendency to classify random variants in non-cancer genes as deleterious. Challenges include the absence of consensus on informative features, diverse training datasets, and restriction to well annotated proteins/transcripts. Balancing the sensitivity and false positives in detecting cancer drivers is crucial. Integrating individual prediction scores with ML algorithms enhances tool performance but comes with risk of error propagation, and limited accuracy. The study emphasises the need for context-specific variant classification tools, as many variants' impacts are cancer-type specific, and some may drive disease synergistically. Existing tools, designed for a "one variant - one score approach," struggle to capture complex associations, especially those dependent on changes in the tumour microenvironment. Highlighting areas for improvement, the study addresses the "black box" problem in decision processes. While limited interpretability might not hinder practical applications, tools should evolve to assess more complex associations guided by biology. Formal consensus, reference training datasets, and standards are deemed essential for developing next-generation tools. The envisioned context-dependent tools aim to streamline feature complexity, thereby mitigating the black box problem and advancing the accuracy and interpretability of cancer variant annotation. Citation Format: Madhumita, Zbyslaw Sondka, Jon Teague. Evaluating the utility of in silico variant annotation tools for cancer driver detection [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 4884.

Abstract 3500: Refining diagnosis of renal cell carcinoma subtypes through single-cell resolution transcriptomic signatures

Abstract A remaining challenge in the field of renal cell carcinoma (RCC) is diagnosis of rare or mixed subtypes, which hampers the delivery of the most effective healthcare to patients. While a new classification scheme, integrating molecular characteristics of tumors, is emerging, it requires a deep understanding of the tumor heterogeneity and etiopathogenesis of each subtype. We have generated and analyzed single-cell transcriptome profiles of 38 human RCCs and normal kidneys 16 RCCs with diverse histological subtypes and established gene signatures specific to each cell type. We have applied these gene signatures to bulk RNA-sequencing data of 1000 RCC tumors with different histological subtypes and normal kidneys from The Cancer Genome Atlas (TCGA) to characterize different cancer types and cellular origins. Unsupervised clustering analysis of TCGA data shows diagnostic discrepancies for 9.3% cases and subclassifies each major subtype (clear cell, papillary and chromophobe) into clusters with differential clinical outcomes.[KM1]. The pathological marker expression in misdiagnosed tumors is consistent with clustering-based classification, not their original diagnosis. Notably, using unique and subtype-specific gene signature patterns, we identified clear cell papillary renal cell tumor (ccpRCT), which is a newly classified benign entity and the main tumor type representing the mis-diagnosed tumors. These observations have further been confirmed by reviewing histological images of the tumors by an experienced genitourinary pathologist. Interestingly, our gene signatures reveal that ccpRCT does not originate from proximal tubule cells unlike clear cell and papillary RCCs. Taken together, our approach suggests a potential new classification method connected to clinical behaviors and drug responses based on overall transcriptomic differences. Citation Format: Minjun Kim, Ariel Madrigal, Zohreh Mehrjoo, Kate I. Glennon, Madeleine Arseneault, Morag Park, Fadi Brimo, Simon Tanguay, Hamed S. Najafabadi, Yasser Riazalhosseini. Refining diagnosis of renal cell carcinoma subtypes through single-cell resolution transcriptomic signatures [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 3500.

In vivo evaluation of a Nano-enabled therapeutic vitreous substitute for the precise delivery of triamcinolone to the posterior segment of the eye

This study focused on the design of a thermoresponsive, nano-enabled vitreous substitute for the treatment of retinal diseases. Synthesis of a hydrogel composed of hyaluronic acid and a poloxamer blend was undertaken. Poly(D,L-lactide-co-glycolide) acid nanoparticles encapsulating triamcinolone acetonide (TA) were synthesised with a spherical morphology and mean diameter of ~ 153 nm. Hydrogel fabrication and nanoparticle loading within the hydrogel was confirmed via physicochemical analysis. Gelation studies indicated that hydrogels formed in nine minutes and 10 min for the unloaded and nanoparticle-loaded hydrogels, respectively. The hydrogels displayed in situ gel formation properties, and rheometric viscoelastic studies indicated the unloaded and loaded hydrogels to have modulus values similar to those of the natural vitreous at 37 °C. Administration of the hydrogels was possible via 26G needles allowing for clinical application and drug release of triamcinolone acetonide from the nanoparticle-loaded hydrogel, which provided sustained in vitro drug release over nine weeks. The hydrogels displayed minimal swelling, reaching equilibrium swelling within 12 h for the unloaded hydrogel, and eight hours for the nanoparticle-loaded hydrogel. Biodegradation in simulated vitreous humour with lysozyme showed < 20% degradation within nine weeks. Biocompatibility of both unloaded and loaded hydrogels was shown with mouse fibroblast and human retinal pigment epithelium cell lines. Lastly, a pilot in vivo study in a New Zealand White rabbit model displayed minimal toxicity with precise, localised drug release behaviour, and ocular TA levels maintained within the therapeutic window for the 28-day investigation period, which supports the potential applicability of the unloaded and nanoparticle-loaded hydrogels as vitreous substitutes that function as drug delivery systems following vitrectomy surgery.Graphical Fig. aSchematic representation of the nano-enabled therapeutic vitreous substitute synthesis process. This figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license

Exploring the NRF2-TP53 Signaling Network Through Machine Learning and Pan-Cancer Analysis: Identifying Potential targets for Cancer Prognosis Related to Oxidative Stress.

Oxidative stress (OXS) is closely related to tumor prognosis and immune response, while TP53 integrated with NRF2 is closely associated with the regulation of cancer-related OXS. Hence, constructing a TP53-NRF2 integrated OXS signature of pan-cancer is essential in predicting survival prognosis and facilitating cancer drug treatment. The pan-cancer analysis acquired the Cancer Genome Atlas (TCGA) transcriptome sequencing data from UCSC Xena, which consisted of 33 cancer types (n = 10440). The Random Forest, Lasso regression, and Cox regression analyses are used to construct an OXS score based on 25 OXS genes. Following this, based on the OXS signature, patients are categorized into low- and high-risk groups. The disparities between the two cohorts regarding survival prognosis, immune infiltration, and drug sensitivity are delved deeply. The expression level of genes is confirmed using immunohistochemistry. The prognosis of pan-cancer patients is adequately predicted by the OXS signature with the assistance of the machine-learning algorithm. A highly accurate nomogram is developed by combining the OXS signature and clinical features. The presence of immune cells indicated that the OXS signature can be associated with the critical pathways of immunotherapy for all types of cancer, and BCL2 showed promising results. Distinct inter-group differences are observed in the OXS signature for frequently utilized antineoplastic medications in clinical settings, including first-line drugs suggested in the guidelines. In summary, by conducting a thorough analysis of OXS genes, a new model based on OXSscore is successfully developed. This model can predict the clinical prognosis and drug sensitivity of pan-cancer with high accuracy. Potential stars in the field of cancer-related anti-OXS may include drugs that target BCL2.

Abstract 907: ScreenDL: A transfer learning framework integrating tumor omics and functional drug screening for personalized clinical drug response prediction

Abstract Precision oncology hinges on accurate prediction of patient-specific treatment response from tumor molecular inputs. While deep learning (DL) models achieve state-of-the-art response prediction in cell lines, existing methods do not readily translate to the clinic where training data is limited. Here, we have developed and validated ScreenDL, a novel DL framework designed explicitly for use in clinical precision oncology applications. The underlying architecture of ScreenDL consists of two fully connected branches dedicated to learning rich embeddings of a drug’s chemical structure and a tumor’s transcriptomic profile respectively. These tumor omic and drug molecular embeddings are fed into a shared response prediction subnetwork which outputs predicted ln(IC50) values. The ScreenDL training schema incorporates three phases, each designed to improve clinical response prediction: (1) an initial pretraining phase leverages large-scale cell line pharmacogenomic datasets to extract patterns/relationships linking tumor omics and drug response; (2) subsequent transfer learning integrates pharmacogenomic data from patient-derived models of cancer, adapting the pretrained model to a more patient-relevant context; (3) patient-level omics and drug screening data (e.g., from matched patient-derived organoid (PDO) models) is integrated through patient-specific fine-tuning, generating a patient-specific response prediction model. Importantly, biomaterial from surgical tumor resection is often available for organoid establishment and patient-specific drug screening in practical clinical scenarios. To assess the utility of our ScreenDL framework for clinical response prediction, we applied ScreenDL to predict treatment response in 50 advanced/metastatic triple negative breast cancer (TNBC) patient-derived xenograft models (PDX). We leveraged tumor omics profiles for each PDX and drug screening data from matched PDX-derived organoids (PDxOs), mirroring the combination of patient-level tumor omic characterization and functional drug screening in matched PDOs, a protocol currently being piloted in Functional Precision Oncology trials at the University of Utah Huntsman Cancer Institute. After cell line pretraining, ScreenDL achieves a modest median Pearson correlation per drug of 0.15 relative to 0.03 for the leading compared model. However, transfer learning and subsequent PDX-specific fine-tuning significantly improve performance, producing median Pearson correlations per drug of 0.39 and 0.51, respectively. These results demonstrate the ability of our ScreenDL framework to dramatically improve response prediction in clinically relevant domains, bringing DL-based precision treatment selection closer to clinical application. Citation Format: Casey Sederman, Tonya Di Sera, Yi Qiao, Xiaomeng Huang, Bryan E. Welm, Alana L. Welm, Gabor Marth. ScreenDL: A transfer learning framework integrating tumor omics and functional drug screening for personalized clinical drug response prediction [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 907.

Abstract 6767: Multivariate analysis of ex vivo kidney 3D immune-microtumors for functional precision medicine

Abstract Immuno-oncology (IO) represents a significant paradigm shift in cancer treatment, focused on leveraging and mobilizing a patient’s own immune response and anti-tumor capabilities. Despite accounting for the majority of available treatment options, there are currently no diagnostic tests capable of predicting combination immunotherapy response in kidney cancer. We have developed a fully human 3D IO model for clinical drug efficacy prediction through multivariate analysis of the tumor-immune microenvironment (TiME) combining functional assays, artificial intelligence and multi-omics. These immune-microtumors recapitulate a patient’s unique pathophysiology and allow 3D spatio-temporal analyses of functional metrics at both bulk and single-cell resolutions. Renal cell carcinoma (RCC) resections and matched blood were collected and processed for single cell isolation of tumor and PBMCs (N=20, ISRCTN10001405). Flow cytometry (FC) and immunofluorescence (IF) were used to characterize TiME cell subpopulations (cancer, immune, stromal etc.) in pellets and 3D cultures, and to deconvolute bulk RNAseq data. Target (tumor) and effector cells (PBMCs and CD8+) were stained with fluorescent probes, activated using ɑCD3 and encapsulated in TiME-mimicking 3D matrices. 3D immune-microtumors were treated with FDA-approved regimens including immunotherapies (ipilimumab, nivolumab, pembrolizumab) and receptor-tyrosine kinase inhibitors (TKIs)(cabozantinib, lenvatinib, axitinib) as monotherapies or combination therapies. Differential drug efficacies were functionally quantified over 4 days using 3D confocal microscopy and computer vision pipelines to extract cell behavior metrics such as immune cell infiltration, immune/tumor cell migration speed, and tumor killing/viability. We devised a specialized RCC hydrogel formulation with superior viability compared to MatrigelTM alone (N=7). FC/IF/RNAseq were used to quantify relative cell subpopulations after isolation and show protein/transcriptional signature retention after 3D culture (N=7). The platform was conducive to the testing of various drugs with varying MoAs including immunotherapies, TKIs, targeted therapies and cell therapies. Treatment with pembrolizumab led to 26% higher PBMC infiltration, 14% higher CD8+ infiltration and 15% increased tumor cell death compared to control. When in combination, pembrolizumab reduced cell viability by 10% and 19% with lenvatinib and axitinib respectively (N=1). Population analyses revealed intra-patient response heterogeneity (N=7). In conclusion, we showed MoA-agnostic, patient-dependent responses to FDA-approved clinical treatment regimens via molecular and functional spatio-temporal analyses. Clinical outcome correlation is currently underway in our advanced RCC study PEAR-TREE2 as well as in breast NCT05435352 and brain NCT06038760 tumors. Citation Format: Eleonora Peerani, Keqian Nan, Demi A. Wiskerke, Thomas D. Richardson, Polina Maximchick, Jay Kearney, George R. de Fraine, Kerrie L. Loughrey, Jonathan Ient, Jessica A. Hudson, Andreas Kaffa, Aston M. Crawley, Nourdine K. Bah, Edgar G. Lopez Molina, Elli Tham, Francesco Iori, Matthew H. Williams, Maxine G. Tran, Duleek N. Ranatunga. Multivariate analysis of ex vivo kidney 3D immune-microtumors for functional precision medicine [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 6767.

Review article: New developments in biomarkers and clinical drug development in alpha-1 antitrypsin deficiency-related liver disease.

Alpha-1 antitrypsin liver disease (AATLD) occurs in a subset of patients with alpha-1 antitrypsin deficiency. Risk factors for disease progression and specific pathophysiologic features are not well known and validated non-invasive assessments for disease severity are lacking. Currently, there are no approved treatments for AATLD. To outline existing understanding of AATLD and to identify knowledge gaps critical to improving clinical trial design and development of new treatments. This report was developed following a multi-stakeholder forum organised by the Alpha-1 Antitrypsin Deficiency Related Liver Disease Expert Panel in which experts presented an overview of the available literature on this topic. AATLD results from a 'gain of toxic function' and primarily manifests in those with the homozygous Pi*ZZ genotype. Accumulation of misfolded 'Z' AAT protein in liver cells triggers intracellular hepatocyte injury which may ultimately lead to hepatic fibrosis. Male gender, age over 50 years, persistently elevated liver tests, concomitant hepatitis B or C virus infection, and metabolic syndrome, including obesity and type 2 diabetes mellitus, are known risk factors for adult AATLD. While the gold standard for assessing AATLD disease activity is liver histology, less invasive measures with low intra- and inter-observer variability are needed. Measurement of liver stiffness shows promise; validated thresholds for staging AATLD are in development. Such advances will help patients by enabling risk stratification and personalised surveillance, along with streamlining the development process for novel therapies. This inaugural forum generated a list of recommendations to address unmet needs in the field of AATLD.

Abstract 1180: Asparagine deprivations enhance CD8+ T cell antitumor activity and ICB immunotherapy via ROS-mediated NFAT nuclear localization

Abstract Metabolic reprogramming plays a crucial role in the differentiation and activation of T cells. Both oxidative phosphorylation (OXPHOS) and glycolysis are upregulated to meet the high energy demands. However, the impact of amino acid metabolism on T cell activation remains unclear. Our study demonstrates that asparagine deprivation enhances the antitumor activity of CD8+ cytotoxic T lymphocytes (CTLs) and increases the flux of glycolysis and the tricarboxylic acid (TCA) cycle. Mechanistically, asparagine deprivation induces the production of reactive oxygen species (ROS) through mitochondrial complex I deficiency and leads to nuclear localization of NFAT, subsequently affecting cytokine production and T cell activation. In the animal model, we found that the clinical drug leunase exhibits antitumor activity that relies on the immune response of T cells. Leunase treatment promotes the activation of tumor-infiltrating lymphocytes (TILs) in vivo. Furthermore, the combination therapy of leunase and anti-PD1 demonstrates a synergistic effect. In patients with nasopharyngeal carcinoma (NPC), leunase therapy enhances the cytokines production and activation of CD8+ T cell. In summary, our findings offer valuable insights into how T cells adapt to changes in their nutritional environment. Citation Format: Hsuan Chia Chang, Kwang-Huei Lin, Huang Yu Yang. Asparagine deprivations enhance CD8+ T cell antitumor activity and ICB immunotherapy via ROS-mediated NFAT nuclear localization [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 1180.

Abstract 1459: The immuno-AVI-PDX, am innovative patient-derived xenograft model for preclinical immuno-oncology studies

Abstract Over the last decade, the success of immunotherapy in treating numerous solid and hematological tumors is unprecedent. However, the poor preclinical predictive power of current model systems explains in part the failures during late-stages clinical drug development for the numerous potential immunotherapies. Thus, there is a crucial need for innovative technologies offering fast and reliable evaluation of immunotherapies on patient-derived models. Using its proprietary process of micro-implantation of patient tumor cells within selected organs of series of avian embryos, Oncofactory developed two humanized avian xenograft models, namely IMMUNO-AVI-patient derived xenograft (iAVI-PDXTM) and IMMUNO-AVI-cell line derived xenograft (iAVI-cellDXTM). These models, consisting in the co-implantation of human peripheral-blood mononuclear cells (PBMC) and human cancer cells were validated through a sequence of experimental steps. First, we found that transplantation of PBMCs into avian tissues exhibited good survival and low allogenicity against the avian embryonic organism. Second, in embryos engrafted with tumoral cells, we established that intravenously injected PBMCs successfully populated the tumor. Third, to reduce the dilution of immune cells in the embryonic tissues and to better standardize the number of implanted cells in between embryos, we optimized the process by direct co-implantation of both PBMCs and cancer cells. We analyzed the immune cell contingent sorted from dissected tumors and found that all major immune cells populations, including T and B lymphocytes, monocytes as well as NK cells were preserved post-grafting, when compared to pre-grafted PBMCs. The AVI-cellDXTM and AVI-PDXTM models initially developed by Oncofactory allows fast and reproducible tumor growth with statistical evaluation of molecule efficacy already at 24 hours post-injection. To assess whether our humanized models retain these advantages, we analyzed the outcome of anti-PD1 antibody (Pembrolizumab) intravenous injection in series of avian embryos co-engrafted with human PBMCs and PDL1-expressing breast and colorectal cancer cell lines. Twenty-four hours post vehicle or Pembrolizumab exposure, we analyzed the tumor volumes in grafted embryos using light sheet microscopy. In all three cancer models, we found a statistically significant reduction of the volume upon pembrolizumab, compared with vehicle. Using a panel of molecular markers in flow cytometry, we found that the Pembrolizumab efficiently targeted T lymphocytes in the tumors. Finally, we validated the efficiency of Pembrolizumab treatment in models of co-implantation of human PBMCs with melanoma and colorectal patient samples. Thus, humanized iAVI-cellDXTM or -PDXTM technology bring powerful and fast alternative to mouse models, to strongly accelerate preclinical developments in immuno-oncology. Citation Format: Marjorie Lacourrege, Clélia Costechareyre, Loraine Jarrosson, Romain Teinturier, Frédéric Berget, Céline Delloye-Bourgeois, Valérie Castellani. The immuno-AVI-PDX, am innovative patient-derived xenograft model for preclinical immuno-oncology studies [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 1459.