Drug Research And Development Research Articles

Enhancing the Intrinsic Antiplasmodial Activity and Improving the Stability and Selectivity of a Tunable Peptide Scaffold Derived from Human Platelet Factor 4.

The control of malaria, a disease caused by Plasmodium parasites that kills over half a million people every year, is threatened by the continual emergence and spread of drug resistance. Therefore, new molecules with different mechanisms of action are needed in the antimalarial drug development pipeline. Peptides developed from host defense molecules are gaining traction as anti-infectives due to theood of inducing drug resistance. Human platelet factor 4 (PF4) has intrinsic activity against P. falciparum, and a macrocyclic helix-loop-helix peptide derived from its active domain recapitulates this activity. In this study, we used a stepwise approach to optimize first-generation PF4-derived internalization peptides (PDIPs) by producing analogues with substitutions to charged and hydrophobic amino acid residues or with modifications to terminal residues including backbone cyclization. We evaluated the in vitro activity of PDIP analogues against P. falciparum compared to their overall helical structure, resistance to breakdown by serum proteases, selective binding to negatively charged membranes, and hemolytic activity. Next, we combined antiplasmodial potency-enhancing substitutions that retained favorable membrane and cell-selective properties onto the most stable scaffold to produce a backbone cyclic PDIP analogue with four-fold improved activity against P. falciparum compared to first-generation peptides. These studies demonstrate the ability to modify PDIP to select for and combine desirable properties and further validate the suitability of this unique peptide scaffold for developing a new molecule class that is distinct from existing antimalarial drugs.

Uncovering the Elusive Structures and Mechanisms of Prevalent Antidepressants

AbstractMost treatments to alleviate major depression work by either inhibiting human monoamine transporters, vital for the reuptake of monoamine neurotransmitters, or by inhibiting monoamine oxidases, which are vital for their degradation. The analysis of the experimental 3D structures of those antidepressants in their drug formulation state is key to precision drug design and development. In this study, microcrystal electron diffraction (MicroED) is applied to reveal the atomic 3D structures for the first time of five of the most prevalent antidepressants (reboxetine, pipofezine, ansofaxine, phenelzine, and bifemelane) directly from the commercially available powder of the active ingredients. Their modes of binding are investigated by molecular docking, revealing the essential contacts and conformational changes into the biologically active state. This study underscores the combined use of MicroED and molecular docking to uncover elusive drug structures and mechanisms to aid in further drug development pipelines.

Artificial intelligence for ophthalmic drug discovery and development: Capabilities, applications, and challenges

The integration of artificial intelligence (AI) into ophthalmic drug discovery and development presents transformative opportunities to address the inherent complexities and challenges of creating targeted therapies for eye diseases. The ability of AI to process vast datasets can facilitate the discovery of novel drug candidates, improve predictions of drug efficacy and safety, and streamline the drug development pipeline. Applications can range from enhancing target identification and compound screening to refining predictive toxicology. However, challenges such as data limitations, computational demands, model interpretability, and ethical considerations remain. Despite these hurdles, the integration of AI with emerging technologies and its potential to optimize clinical trials signifies a new era of innovation in ophthalmology, emphasizing its critical role in addressing current challenges and advancing therapeutic development. In this paper, we explore the role of AI in ophthalmic drug discovery, highlighting its potential to address critical challenges in the field and delineating its impact across various stages of drug development.

Parkinson's Disease Drug Therapies in the Clinical Trial Pipeline: 2024 Update.

For the past five years, our annual reports have been tracking the clinical development of new drug-based therapies for the neurodegenerative condition of Parkinson's disease (PD). These reviews have followed the progress both of "symptomatic treatments" (ST - improves/reduces symptoms of the condition) and "disease-modifying treatments" (DMT - attempts to delay/slow progression by addressing the underlying biology of PD). Efforts have also been made to further categorize these experimental treatments based on their mechanisms of action and class of drug. A dataset of clinical trials for drug therapies in PD using trial data downloaded from the ClinicalTrials.gov online registry was generated. A breakdown analysis of all the studies that were active as of January 31st, 2024, was conducted. This analysis involved categorizing the trials based on both the mechanism of action (MOA) and the drug target. There were 136 active Phase 1-3 trials evaluating drug therapies for PD registered on ClinicalTrials.gov, as of January 31, 2024. Of these trials, 76 (56%) were classified as ST trials and 60 (44%) were designated DMT. More than half (58%) of the trials were in Phase 2 testing stage, followed by Phase 1 (30%) and Phase 3 (12%). 35 of the trials were registered since our last report, with the remaining 101 trials appearing in at least one earlier report. The drug development pipeline for PD remains in a robust state with a wide variety of approaches being developed and evaluated in Phase 1 and 2. Yet again, however, only a limited number of DMTs are transitioning to Phase 3.

Emodin inhibits respiratory syncytial virus entry by interactions with fusion protein.

Respiratory syncytial virus (RSV) fusion (F) protein is essential for facilitating virus entry into host cells, providing a hopeful path for combating viral diseases. However, F protein inhibitors can rapidly select for viral resistance. Thus, discovering new inhibitors of F-protein is necessary to enrich the RSV drug development pipeline. In this study, we screen 25 bioactive compounds from Chinese herbal medicines that exhibit a strong binding to the RSV-F protein using surface plasmon resonance. After screening, we found emodin could strongly bind to RSV-F protein, and could effectively curb RSV infection. Further investigations certificated that emodin specifically disrupts the attachment and internalization phases of RSV infection by targeting the RSV-F protein. In vivo studies with mice infected with RSV demonstrated that emodin effectively reduces lung pathology. This therapeutic effect is attributed to emodin's capacity to diminish pro-inflammatory cytokine production and reduce viral load in the lungs. In conclusion, our findings provide initial insights into the mechanism by which emodin counters RSV infection via engagement with the RSV-F protein, establishing it as a viable contender for the development of novel therapeutic agents aimed at RSV.

Guiding organs-on-chips towards applications: a balancing act between integration of advanced technologies and standardization

Organs-on-chips (OoC) are in vitro models that emulate key functionalities of tissues or organs in a miniaturized and highly controlled manner. Due to their high versatility, OoC have evolved as promising alternatives to animal testing for a more effective drug development pipeline. Additionally, OoC are revealing increased predictive power for toxicity screening applications as well as (patho-) physiology research models. It is anticipated that enabling technologies such as biofabrication, multimodality imaging, and artificial intelligence will play a critical role in the development of the next generation of OoC. These domains are expected to increase the mimicry of the human micro-physiology and functionality, enhance screening of cellular events, and generate high-content data for improved prediction. Although exponentially growing, the OoC field will strongly benefit from standardized tools to upgrade its implementational power. The complexity derived from the integration of multiple technologies and the current absence of concrete guidelines for establishing standards may be the reason for the slower adoption of OoC by industry, despite the fast progress of the field. Therefore, we argue that it is essential to consider standardization early on when using new enabling technologies, and we provide examples to illustrate how to maintain a focus on technology standards as these new technologies are used to build innovative OoC applications. Moreover, we stress the importance of informed design, use, and analysis decisions. Finally, we argue that this early focus on standards in innovation for OoC will facilitate their implementation.

Spectrum of activity and mechanisms of azole-bisphosphonate synergy in pathogenic Candida.

Candida is a common and often very serious opportunistic fungal pathogen. Invasive candidiasis is a prevalent cause of nosocomial infections with a high mortality rate, and mucocutaneous infections significantly impact the quality of life of millions of patients a year. These infections pose substantial clinical challenges, particularly as the currently available antifungal treatment options are limited in efficacy and often toxic. Azoles are a mainstay of antifungal therapy and work by targeting the biosynthesis of ergosterol. However, there are rising rates of acquired azole resistance in various Candida species, and some species are considered intrinsically resistant to most azoles. Our research demonstrates the promising therapeutic potential of synergistically enhancing azoles with non-toxic, FDA-approved bisphosphonates. Repurposing bisphosphonates as antifungal synergists can bypass much of the drug development pipeline and accelerate the translation of azole-bisphosphonate combination therapy.

QLDTI: A Novel Reinforcement Learning-based Prediction Model for Drug-Target Interaction

Background: Predicting drug-target interaction (DTI) plays a crucial role in drug research and development. More and more researchers pay attention to the problem of developing more powerful prediction methods. Traditional DTI prediction methods are basically realized by biochemical experiments, which are time-consuming, risky, and costly. Nowadays, DTI prediction is often solved by using a single information source and a single model, or by combining some models, but the prediction results are still not accurate enough. Objective: The study aimed to utilize existing data and machine learning models to integrate heterogeneous data sources and different models, further improving the accuracy of DTI prediction. Methods: This paper has proposed a novel prediction method based on reinforcement learning, called QLDTI (predicting drug-target interaction based on Q-learning), which can be mainly divided into two parts: data fusion and model fusion. Firstly, it fuses the drug and target similarity matrices calculated by different calculation methods through Q-learning. Secondly, the new similarity matrices are inputted into five models, NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, for further training. Then, all sub-model weights are continuously optimized again by Q-learning, which can be used to linearly weight all sub-model prediction results to output the final prediction result. Results: QLDTI achieved AUC accuracy of 99.04%, 99.12%, 98.28%, and 98.35% on E, NR, IC, and GPCR datasets, respectively. Compared to the existing five models NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, the QLDTI method has achieved better results on four benchmark datasets of E, NR, IC, and GPCR. Conclusion: Data fusion and model fusion have been proven effective for DTI prediction, further improving the prediction accuracy of DTI.

Modern Medicine and Pharmaceutics

Modern Medicine and pharmaceuticals have made tremendous strides in enhancing the healthcare and quality of life of people worldwide. This summary explores the key traits and trends in these fields. In recent decades, current medications have witnessed a paradigm shift toward personalized healthcare. Improvements in genomics, proteomics, and record analytics have paved the way for precision medication and tailoring treatments for an individual's genetic makeup. This technique enhances treatment efficacy while minimizing aspect consequences, marking a significant departure from the one-size-fits-all approach. Pharmaceutical research has experienced a revolution with the advent of biotechnology. Biopharmaceuticals, along with monoclonal antibodies and gene treatment plans, have emerged as powerful tools for the fight against formerly untreatable illnesses. Their ability to target specific molecules or genes has opened new frontiers in cancer treatment, uncommon illnesses, and autoimmune diseases. Furthermore, pharmaceutical enterprises have embraced virtual technologies. AI-pushed drug discovery, in silico medical trials, and telemedicine reshape the drug development pipeline and improve the care of affected persons. In particular, telemedicine has gained prominence, presenting available healthcare offerings and far-flung tracking. However, these advancements have moral and regulatory demands. Privateness issues, information protection, and equitable access to present-day remedies remain essential issues. The need for strong moral frameworks and regulatory oversight is paramount

Comparison of Human Long-Term Liver Models for Clearance Prediction of Slowly Metabolized Compounds.

The accurate prediction of human clearance is an important task during drug development. The proportion of low clearance compounds has increased in drug development pipelines across the industry since such compounds may be dosed in lower amounts and at lower frequency. These type of compounds present new challenges to in vitro systems used for clearance extrapolation. In this study, we compared the accuracy of clearance predictions of suspension culture to four different long-term stable in vitro liver models, including HepaRG sandwich culture, the Hµrel stochastic co-culture, the Hepatopac micropatterned co-culture (MPCC), and a micro-array spheroid culture. Hepatocytes in long-term stable systems remained viable and active over several days of incubation. Although intrinsic clearance values were generally high in suspension culture, clearance of low turnover compounds could frequently not be determined using this method. Metabolic activity and intrinsic clearance values from HepaRG cultures were low and, consequently, many compounds with low turnover did not show significant decline despite long incubation times. Similarly, stochastic co-cultures occasionally failed to show significant turnover for multiple low and medium turnover compounds. Among the different methods, MPCCs and spheroids provided the most consistent measurements. Notably, all culture methods resulted in underprediction of clearance; this could, however, be compensated for by regression correction. Combined, the results indicate that spheroid culture as well as the MPCC system provide adequate in vitro tools for human extrapolation for compounds with low metabolic turnover. SIGNIFICANCE STATEMENT: In this study, we compared suspension cultures, HepaRG sandwich cultures, the Hµrel liver stochastic co-cultures, the Hepatopac micropatterned co-cultures (MPCC), and micro-array spheroid cultures for low clearance determination and prediction. Overall, HepaRG and suspension cultures showed modest value for the low determination and prediction of clearance compounds. The micro-array spheroid culture resulted in the most robust clearance measurements, whereas using the MPCC resulted in the most accurate prediction for low clearance compounds.

Abstract LB435: Large panel organoid drug testing combined with biomarker analysis: Unveiling prospects for mechanism of action identification and preclinical patient stratification

Abstract Introduction The development of oncology drugs has a persistently high attrition rate of greater than 95%, requiring the development and implementation of novel models and technologies aimed at better predictive abilities during the preclinical phase. Among these, organoid technologies, which involve propagating tumor stem cells in a near-native state, have proven to markedly advance drug testing with enhanced precision. Our previously developed assay-ready organoid technology has paved the way for large panel organoid screening, allowing organoids to augment the efficiency of drug development pipelines, boost patient-derived model usage in preclinical studies, and facilitate accurate predictions of drug action mechanisms. Here, we present a proof-of-concept (POC) large scale organoid experiment, studying the responses of 50 models to the cytotoxic agent paclitaxel, using next generation sequencing data to analyze the drug mechanism of action (MOA) and identify biomarkers that predict drug responses. Methods A panel of 50 organoid models was characterized for paclitaxel response in 9-dose-response curves by measuring ATP levels after a 5-day exposure. Models were genomically profiled by whole-exome sequencing (n = 47) and whole-transcriptome sequencing (n = 44) to analyze the association between organoid pharmacology data and genomic information and elucidate genes and pathways that may be related with, or predictive of, the treatment responses. We performed biomarker analysis on gene expression, mutation, and copy number alteration levels. Results By assessing organoid sensitivity to paclitaxel, we observed no significant indication-specific difference in organoid responses across 7 cancer types. The 50 models were then categorized into responders (AUC < 1.5; n=15) and non-responders (AUC > 2.6; n=14) for biomarker discovery. Differential expression analysis and pathway activation analysis revealed genes and pathways involved in the cell cycle and DNA replication. DNA recombination differentiates responders from non-responders, while genes involved in epithelial cell proliferation, differentiation, and wound healing are associated with drug resistance. Mutation of proto-oncogene RB Transcriptional Corepressor 1, RB1 (a negative regulator of the cell cycle), was found to be a biomarker enriched in non-responders to paclitaxel, corroborating earlier clinical findings. Using the identified profile, we predicted responses for non-tested models in our biobank and validated our approach. Conclusion Large panel organoid drug screening in combination with biomarker analysis brings great opportunities for increasing drug pipeline efficiency. The POC here presented highlights how such a screen can identify MOAs and identify relevant biomarker profiles to be used in clinical trial patient stratification. Citation Format: Linda Xue, Liza Wijler, Marten Hornsveld, Sheng Guo, Leo Price, Marrit Putker, Ludovic Bourre. Large panel organoid drug testing combined with biomarker analysis: Unveiling prospects for mechanism of action identification and preclinical patient stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB435.

2022–2023 Drug Updates: Investigational Therapeutics in the Pipeline

During JADPRO Live 2023, Sandra Cuellar, PharmD, BCOP, FHOPA, FASHP, discussed investigational therapeutic agents in the drug development pipeline. Dr. Cuellar highlighted new drug classes, novel mechanisms of action, and safety profiles that advanced practitioners should be aware of.

Alzheimer's disease drug development pipeline: 2024

New therapies to prevent or delay the onset of symptoms, slow progression, or improve cognitive and behavioral symptoms of Alzheimer's disease (AD) are needed. We interrogated clinicaltrials.gov including all clinical trials assessing pharmaceutical therapies for AD active in on January 1, 2024. We used the Common Alzheimer's Disease Research Ontology (CADRO) to classify the targets of therapies in the pipeline. There are 164 trials assessing 127 drugs across the 2024 AD pipeline. There were 48 trials in Phase 3 testing 32 drugs, 90 trials in Phase 2 assessing 81 drugs, and 26 trials in Phase 1 testing 25 agents. Of the 164 trials, 34% (N = 56) assess disease-modifying biological agents, 41% (N = 68) test disease-modifying small molecule drugs, 10% (N = 17) evaluate cognitive enhancing agents, and 14% (N = 23) test drugs for the treatment of neuropsychiatric symptoms. Compared to the 2023 pipeline, there are fewer trials (164vs. 187), fewer drugs (127vs. 141), fewer new chemical entities (88vs. 101), and a similar number of repurposed agents (39vs. 40). In the 2024 Alzheimer's disease drug development pipeline, there are 164 clinical trials assessing 127 drugs.The 2024 Alzheimer's disease drug development pipeline has contracted compared to the 2023 Alzheimer pipeline with fewer trials, fewer drugs, and fewer new chemical entities.Drugs in the Alzheimer's disease drug development pipeline target a wide array of targets; the most common processes targeted include neurotransmitter receptors, inflammation, amyloid, and synaptic plasticity.The total development time for a potential Alzheimer's disease therapy to progress from nonclinical studies to FDA review is approximately 13 years.

Abstract 244: Patient-derived organoids: A pre-clinical model to assess infection and killing capacity of viral vectors for cancer therapy

Abstract HUB’s patient-derived organoids (PDOs) are in vitro models that preserve tissue morphology and original cellular complexity. Additionally, PDOs are compatible with (high) throughput screening methods, enabling informed decision-making early in the drug development pipeline and significantly improving the odds of positive outcomes in clinical trials. Akamis Bio has developed experimental viral vector-based tumour gene therapies that have the potential to treat a variety of solid tumours by driving the expression of immunologically active biomolecules and therapeutic proteins hampering tumour immune evasion. These vectors are based on a replication competent, chimeric group B adenovirus backbone that selectively replicates and expresses transgene payloads in tumor cells, and additionally exerts oncolytic activity. To evaluate the efficacy of such a virus-based gene delivery strategy, HUB set up a proof-of-concept study to assess the propagation capability of Akamis Bio’s viral vectors in tumour PDOs. Intact PDOs from primary pancreatic tumours were infected with several viral loads, ranging from 0.1 viral particles per cell (PPC) to 100 PPC. The viral vector encoded a GFP reporter gene to identify infected cells using fluorescence imaging. The cultures were imaged daily for 14 days, allowing the assessment of cytotoxicity and infection efficiency and dynamics. Independently of PPC, infections always started in the exterior of PDOs in a few GFP-positive cells. The gradual increase over time in GFP-signal toward the interior of PDOs suggests the high likelihood of secondary infection events. Crucially, GFP intensity was correlated with cell death in PDOs, as evidenced by morphological features detected in brightfield images. Moreover, the infection dynamics were dose-dependent, as the GFP intensity peak arose later with decreasing PPC. These results show the capacity of HUB PDOs to study the infection dynamics of Akamis viral vectors. This pre-clinical platform permits rapid and efficient screening and dose evaluation for oncolytic virotherapies. Unique access to extensive tumour PDO biobanks with characterized genomic landscapes allows best-in-class stratification of treatment response based on tumour type and genetic heterogeneity. Citation Format: Francisco Morales-Rodriguez, Maria Stella Sasso, Taya Bezhaeva, Samantha Bailey-Bucktrout, Ka Wai Lai, Foteini Gkogkou, Sylvia Boj. Patient-derived organoids: A pre-clinical model to assess infection and killing capacity of viral vectors for cancer therapy [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 244.

Abstract 240: Mix and match - A comprehensive pipeline for matched patient-derived PDX and PD3D® modelsfor cancer research and preclinical drug development

Abstract Relevant in vivo and in vitro cancer models have been invaluable for our current understanding of cancer biology, as well as for therapy development. Using appropriate models in the drug development process significantly supports reliable and swift decision making in proceeding along the value chain. In parallel, animal experiments can be designed more specific to the scientific and regulatory needs, resulting in shorter development cycles and eventually the availability of better therapies for cancer patients. Patient-derived xenografts (PDX) are generated by implantation of cancerous tissue from a patient’s tumor either under the skin (ectopic) or into the organ of tumor origin (orthotopic) of an immunocompromised mouse and are the most used in vivo model for preclinical drug development. Patient-derived 3D cell culture models (PD3D®) are gaining increasing significance as relevant in vitro cancer models that recapitulate the original tumor tissue’s biology and are suitable for high-throughput drug screening. The combination of both platform’s increases the translational relevance of the development pipeline and offers the possibility to enlarge the respective panels of tumor models. Tackling an unmet medical need, we initially created matched mesothelioma PD3D® and PDX models. Mesothelioma is an aggressive cancer with poor prognosis and limited treatment options. Development of new therapies is hampered by shortage of available relevant tumor models. We created PD3D® models from PDX mesothelioma models and vice versa and generated drug sensitivity profiles with standard of care such as Gemcitabine, platinum derivates and a number of targeted agents to confirm their suitability as matched models for future projects. We were able to prove that models express stable histological, phenotypic features and drug sensitivity profiles across the two platforms. With this proof-of-concept study, the feasibility to combine the PD3D® biobank comprising more than 500 models from CELLphenomics with the PDX bank from CRL including 700 PDX models, has gained momentum. In consideration of the changing regulatory landscape, the combination of in vitro and in vivo platforms is a crucial step towards a drug development pipeline that incorporates the principles of 3R and increases its translational value due to fully human technologies being the corner stones of the drug development process. Citation Format: Jürgen Loskutov, Eva Oswald, Lena Wedeken, Christoph J. Reinhard, Julia Schueler, Christian R. Regenbrecht. Mix and match - A comprehensive pipeline for matched patient-derived PDX and PD3D® modelsfor cancer research and preclinical 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 240.

Abstract 112: The NSG-Flt3KO; Tg(hFLT3LG); hIL15 mouse strain improves engraftment of innate immune cells post-CD34+ humanization

Abstract Human immune system mouse models play critical roles in numerous preclinical drug development pipelines, including immunology, infectious diseases, and immuno-oncology. These models are constantly evolving, with addition of human cytokines to sustain engraftment of human immune cells. To date, most humanized models have primarily focused on T lymphocyte cell biology, leaving room for improvement in developing models that effectively represent NK cells and the myeloid lineage. Previously, we demonstrated that the introduction of human IL-15 (NSG-IL-15) or FLT3L (NSG-FLT3L) to NOD-scid IL2rγnull (NSG) mice enhanced engraftment of innate immune cells, including human CD56+ NK cells, CD141+ and CD1c+ DC subsets, CD123+ pDCs, and CD14+ monocytes. In this study, we describe a new NSG mouse variant (NSG-Flt3KO; Tg(hFLT3LG); hIL15) that expresses physiological levels of both human IL-15 and FLT3L and detail the immune cell diversity following HSC engraftment. NSG-Flt3KO; Tg(hFLT3LG); hIL15 mice, along with control strains NSG, NSG-IL15, and NSG-FLT3L, were engrafted with human umbilical cord blood derived CD34+ hematopoietic stem cells from three different donors, and blood samples were collected until Week 20 post-transplantation. Blood samples were analyzed via flow cytometry to assess the engraftment of innate and adaptive immune cells. Spleen and bone marrow samples were collected at Week 12 post-transplantation and analyzed for lymphoid and myeloid immune cell populations via flow cytometry. To compare the differences among the strains in terms of immune cell sub-populations across weeks, a Two-Way ANOVA (mixed-model) was conducted, with p<0.05 considered statistically significant. Overall, the results demonstrate that NSG-Flt3KO; Tg(hFLT3LG); hIL15 mice have enhanced CD56+/dim CD16+ NK cells compared to NSG-FTL3L strain (20.5±12.8% vs 7.0±3.5% at Week 12), but comparable to NSG-IL15. Furthermore, myeloid sub-lineage cells in NSG-IL15-FLT3L mice, such as monocytes, macrophages (M1), granulocytes, dendritic cells, and neutrophils, were greater than in NSG or NSG-IL15 strains, but comparable to NSG-FLT3L mice. Overall levels of hCD45+ and T cell sub-populations (CD3+ or CD4+ or CD8+) were comparable across all strains tested. In sum, our NSG-Flt3KO; Tg(hFLT3LG); hIL15 engraftment data suggest an improved model that supports drug development targeting human myeloid and NK cell populations. Citation Format: Srinivas Reddy Boreddy, Shuang Hu, Brian Soper. The NSG-Flt3KO; Tg(hFLT3LG); hIL15 mouse strain improves engraftment of innate immune cells post-CD34+ humanization [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 112.

Abstract 6566: Clinical evaluation of functional combinatorial precision medicine platform to predict treatment outcomes and enhance combination therapy design in soft tissue sarcomas

Abstract Purpose: Soft tissue sarcomas (STS) are a collection of heterogenous cancers with limited range of treatment options and poor outcomes. First line treatment for most STS is anthracycline-based, with gemcitabine-based combinations, pazopanib or ifosfamide as treatment options in refractory/relapsed STS. There is a clinical unmet need to improve treatment options for STS as median time to progression following second- and third-line treatments is less than 4 months. We have previously demonstrated in a 71-patient relapsed/refractory non-Hodgkin’s lymphoma clinical study that a functional precision medicine platform can effectively guide treatment, with several exceptional responders (Science Translational Medicine, 2022). This ex vivo drug sensitivity platform, quadratic phenotypic optimization platform (QPOP), analyses a predesigned array of 155 test combinations performed on a primary patient sample to rank and compare all possible therapeutic combinations from a 12-drug set. In this study, we evaluated clinical concordance of QPOP to predict treatment outcomes in an STS cohort. We also explored QPOP’s combination therapy ranking function to identify novel combinations that may benefit larger groups of STS patients when appropriately guided. Experimental Detail: A 12-drug set comprised of STS standard of care, FDA approved drugs and promising investigational drugs was evaluated in QPOP analysis of primary STS tumor samples. Ex vivo cultures of primary STS samples were treated with a predesigned array of 155 test combinations to provide a quantitative phenotypic output for each test combination. This dataset was analyzed by QPOP to rank all possible drug combinations towards a predicted treatment outcome. Concordance analysis was then performed comparing patient sample results with either parallel treatment initiated at time of sample collection or physician choice to use QPOP-guided treatment. Validation of QPOP-derived top-ranked drug combinations was also performed. Results: 12-drug, 3-level QPOP was successfully performed on 45 of 51 primary patient samples. Clinical concordance analysis showed QPOP had a total predictive value (TPV) of 76.9% and an AUCROC of 85.7%. Exceptional responders to guided treatment with pazopanib or eribulin were observed. Additionally, top-ranked epigenetic-based combinations were identified as effective across multiple STS subtypes and validated preclinically. Conclusion: The results show the potential for QPOP to predict clinical response in solid cancers, such as STS. Furthermore, QPOP can improve drug development pipelines through patient samples-based design of effective combinations with investigational drugs or underutilized drugs that may benefit larger populations of patients or specific subgroups of patients. Citation Format: Edward Kai-Hua Chow, Sharon P. Chan, Tan Boon Toh, Masturah Rashid, Valerie S. Yang. Clinical evaluation of functional combinatorial precision medicine platform to predict treatment outcomes and enhance combination therapy design in soft tissue sarcomas [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 6566.

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.

Repurposing Drugs for Senotherapeutic Effect: Potential Senomorphic Effects of Female Synthetic Hormones.

Repurposing previously approved drugs may fast track the route to the clinic for potential senotherapeutics and improves the inefficiency of the clinical drug development pipeline. We performed a repurposing screen of 240 clinically approved molecules in human primary dermal fibroblasts for their effects on CDKN2A expression. Molecules demonstrating effects on CDKN2A expression underwent secondary screening for senescence-associated beta galactosidase (SAB) activity, based on effect size, direction, and/or molecule identity. Selected molecules then underwent a more detailed assessment of senescence phenotypes including proliferation, apoptosis, DNA damage, senescence-associated secretory phenotype (SASP) expression, and regulators of alternative splicing. A selection of the molecules demonstrating effects on senescence were then used in a new bioinformatic structure-function screen to identify common structural motifs. In total, 90 molecules displayed altered CDKN2A expression at one or other dose, of which 15 also displayed effects on SAB positivity in primary human dermal fibroblasts. Of these, 3 were associated with increased SAB activity, and 11 with reduced activity. The female synthetic sex hormones-diethylstilboestrol, ethynyl estradiol and levonorgestrel-were all associated with a reduction in aspects of the senescence phenotype in male cells, with no effects visible in female cells. Finally, we identified that the 30 compounds that decreased CDKN2A activity the most had a common substructure linked to this function. Our results suggest that several drugs licensed for other indications may warrant exploration as future senotherapies, but that different donors and potentially different sexes may respond differently to senotherapeutic compounds. This underlines the importance of considering donor-related characteristics when designing drug screening platforms.

Treatment Options for Alopecia Areata in Children and Adolescents.

Alopecia areata (AA) lifetime incidence is around 2%, with many patients first experiencing symptoms during childhood. However, ritlecitinib is the only FDA-approved treatment for pediatric patients 12 years and older. This review outlines reported topical, injectable, and oral treatment options for pediatric patients with AA. Clinical studies were obtained via a PubMed search using the following search terms: alopecia areata, areata, universalis, or totalis and medication, therapy, treatment, drug, or management. Only studies with pediatric patients were included in this review. Commonly used therapies, including corticosteroids, methotrexate, and minoxidil, newer promising medications, such as Janus kinase inhibitors, and less frequently used topical and systemic treatments are included. A summary of the drug development pipeline and ongoing interventional clinical trials with pediatric patients is provided. Treatments demonstrate variable efficacy, and many patients require combination therapy for maximal response. More robust clinical data is needed for many of the medications reviewed in order to provide better care for these patients.