Clinical Drug Development Research Articles

Project Optimus Elicits the "Holistic" Benefits of PK/PD Modeling of Immunotherapy.

Leveraging a heterogeneous dataset, a recent article demonstrated the application of pharmacometric modeling to inform the dosing of nivolumab-relatlimab fixed-dose combination in special populations, including adolescents lacking clinical data. The use of model-informed approaches during clinical drug development can be cost-effective, which ensures fast access to drugs and enhances patient outcomes. See related article by Zhao et al., p. 3050.

Investigating gene signatures associated with immunity in colon adenocarcinoma to predict the immunotherapy effectiveness using NFM and WGCNA algorithms.

Colon adenocarcinoma (COAD), a frequently encountered and highly lethal malignancy of the digestive system, has been the focus of intensive research regarding its prognosis. The intricate immune microenvironment plays a pivotal role in the pathological progression of COAD; nevertheless, the underlying molecular mechanisms remain incompletely understood. This study aims to explore the immune gene expression patterns in COAD, construct a robust prognostic model, and delve into the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby providing critical support for individualized treatment strategies and prognostic evaluation. Initially, we curated a comprehensive dataset by screening 2600 immune-related genes (IRGs) from the ImmPort and InnateDB databases, successfully obtaining a rich data resource. Subsequently, the COAD patient cohort was classified using the non-negative matrix factorization (NMF) algorithm, enabling accurate categorization. Continuing on, utilizing the weighted gene co-expression network analysis (WGCNA) method, we analyzed the top 5000 genes with the smallest p-values among the differentially expressed genes (DEGs) between immune subtypes. Through this rigorous screening process, we identified the gene modules with the strongest correlation to the COAD subpopulation, and the intersection of genes in these modules with DEGs (COAD vs COAD vs Normal colon tissue) is referred to as Differentially Expressed Immune Genes Associated with COAD (DEIGRC). Employing diverse bioinformatics methodologies, we successfully developed a prognostic model (DPM) consisting of six genes derived from the DEIGRC, which was further validated across multiple independent datasets. Not only does this predictive model accurately forecast the prognosis of COAD patients, but it also provides valuable insights for formulating personalized treatment regimens. Within the constructed DPM, we observed a downregulation of CALB2 expression levels in COAD tissues, whereas NOXA1, KDF1, LARS2, GSR, and TIMP1 exhibited upregulated expression levels. These genes likely play indispensable roles in the initiation and progression of COAD and thus represent potential therapeutic targets for patient management. Furthermore, our investigation into the molecular mechanisms and therapeutic targets for COAD liver metastasis revealed associations with relevant processes such as fat digestion and absorption, cancer gene protein polysaccharides, and nitrogen metabolism. Consequently, genes including CAV1, ANXA1, CPS1, EDNRA, and GC emerge as promising candidates as therapeutic targets for COAD liver metastasis, thereby providing crucial insights for future clinical practices and drug development. In summary, this study uncovers the immune gene expression patterns in COAD, establishes a robust prognostic model, and elucidates the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby possessing significant theoretical and clinical implications. These findings are anticipated to offer substantial support for both the treatment and prognosis management of COAD patients.

Nanoparticles for the treatment of spinal cord injury.

Spinal cord injuries lead to significant loss of motor, sensory, and autonomic functions, presenting major challenges in neural regeneration. Achieving effective therapeutic concentrations at injury sites has been a slow process, partly due to the difficulty of delivering drugs effectively. Nanoparticles, with their targeted delivery capabilities, biocompatibility, and enhanced bioavailability over conventional drugs, are garnering attention for spinal cord injury treatment. This review explores the current mechanisms and shortcomings of existing treatments, highlighting the benefits and progress of nanoparticle-based approaches. We detail nanoparticle delivery methods for spinal cord injury, including local and intravenous injections, oral delivery, and biomaterial-assisted implantation, alongside strategies such as drug loading and surface modification. The discussion extends to how nanoparticles aid in reducing oxidative stress, dampening inflammation, fostering neural regeneration, and promoting angiogenesis. We summarize the use of various types of nanoparticles for treating spinal cord injuries, including metallic, polymeric, protein-based, inorganic non-metallic, and lipid nanoparticles. We also discuss the challenges faced, such as biosafety, effectiveness in humans, precise dosage control, standardization of production and characterization, immune responses, and targeted delivery in vivo. Additionally, we explore future directions, such as improving biosafety, standardizing manufacturing and characterization processes, and advancing human trials. Nanoparticles have shown considerable progress in targeted delivery and enhancing treatment efficacy for spinal cord injuries, presenting significant potential for clinical use and drug development.

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

Sex and the Kidney Drug-Metabolizing Enzymes and Transporters: Are Preclinical Drug Disposition Data Translatable to Humans?

Cross-species differences in drug transport and metabolism are linked to poor translation of preclinical pharmacokinetic and toxicology data to humans, often resulting in the failure of new chemical entities (NCEs) during clinical drug development. Specifically, inaccurate prediction of renal clearance and renal accumulation of NCEs due to differential abundance of enzymes and transporters in kidneys can lead to differences in pharmacokinetics and toxicity between experimental animals and humans. We carried out liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based protein quantification of 78 membrane drug-metabolizing enzymes and transporters (DMETs) in the kidney membrane fractions of humans, rats, and mice for characterization of cross-species and sex-dependent differences. In general, majority of DMET proteins were higher in rodents than in humans. Significant cross-species differences were observed in 30 out of 33membrane DMET proteins quantified in all three species. Although no significant sex-dependent differences were observed in humans, the abundance of 28 and 46 membrane proteins showed significant sex dependence in rats and mice, respectively. These cross-species and sex-dependent quantitative abundance data are valuable for gaining a mechanistic understanding of drug renal disposition and accumulation. Further, these data can also be integrated into systems pharmacology tools, such as physiologically based pharmacokinetic models, to enhance the interpretation of preclinical pharmacokinetic and toxicological data.

Spectroscopic In-Vitro Drug-Drug Interaction Studies of Amoxicillin and Paracetamol Solid Dosage Forms

Potential drug-drug interactions (DDIs) are a public health concern in clinical therapy and drug development because of their contribution to actual adverse drug reactions (ADRs) or events. The study aimed to quantify the in-vitro drug-drug interaction of amoxicillin (AMX) and paracetamol (PCM) formulations which are commonly co-administered. Determinations were carried out by spectroscopic methods – zero-order and derivative spectroscopy. The data were analyzed using Microsoft Excel, 2016. The UV-Spectra of AMX and PCM showed two distinctive peaks at 240 nm and 260 nm were observed for AMX, while for PCM at 290nm and 340 nm, with maxima at 240 nm and 340 nm for AMX and PCM respectively. Absorption measurements were taken at the maximum wavelength of 290 nm and 240 nm for PCM and AMX respectively. The content of both drugs and potential DDIs of AMX and PCM were assayed by zero- and 3rd-derivative spectroscopy. The calibration curves for AMX and PCM standards showed good linearity at concentrations, with corresponding correlation coefficients (R) being 0.999 and 0.998. The intercept and slope for AMX were -0.0148 and 0.0732 respectively, while for PCM values were 0.0071 and 0.148. The content of PCM and AMX in the formulations was 98.40 ± 0.44% and 96.04 ± 0.29% respectively. The joint-drug-dissolution studies for AMX and PCM interaction (DDI) were obtained with the 3rd-order derivative spectroscopy at 210 nm (zero-crossing for PCM) and 290 nm (zero-line crossing for AMX) respectively - which showed overlapping spectra for their zero-order derivative spectra. No significant difference was observed in the in-vitro drug release profiles for AMX and PCM, while comparative profiles were observed to have a very strong correlation between individual-release and joint-release profiles.

Gaussian accelerated molecular dynamics simulations facilitate prediction of the permeability of cyclic peptides.

Despite their widespread use as therapeutics, clinical development of small molecule drugs remains challenging. Among the many parameters that undergo optimization during the drug development process, increasing passive cell permeability (i.e., log(P)) can have some of the largest impact on potency. Cyclic peptides (CPs) have emerged as a viable alternative to small molecules, as they retain many of the advantages of small molecules (oral availability, target specificity) while being highly effective at traversing the plasma membrane. However, the relationship between the dominant conformations that typify CPs in an aqueous versus a membrane environment and cell permeability remain poorly characterized. In this study, we have used Gaussian accelerated molecular dynamics (GaMD) simulations to characterize the effect of solvent on the free energy landscape of lariat peptides, a subset of CPs that have recently shown potential for drug development (Kelly et al., JACS 2021). Differences in the free energy of lariat peptides as a function of solvent can be used to predict permeability of these molecules, and our results show that permeability is most greatly influenced by N-methylation and exposure to solvent. Our approach lays the groundwork for using GaMD as a way to virtually screen large libraries of CPs and drive forward development of CP-based therapeutics.

Current status and challenges of clinical research and development of new drugs for liver diseases

Liver disease is a serious public health problem worldwide, affecting human health. However, there are still many unmet needs for the treatment of liver disease, especially with new therapeutic drugs. At present, there is no treatment method to eradicate the hepatitis B virus, nor are there therapeutic drugs for liver fibrosis, liver failure, and others. Chemotherapy and targeted immunotherapy are still unsatisfactory for liver cancer. This article provides an overview of the current status and challenges that arise in new drug research and development for liver diseases.

Light‐Driven Multi‐Core/Shell Microdroplets as a Targeted Drug Delivery System

AbstractTargeted drug delivery system (DDS) holds exciting prospects in biomedical clinical research and drug development. However, it faces a significant challenge in achieving stable and safe targeted transportation without drug leakage while obtaining precise and controllable drug dosing with a high drug utilization rate and low side effects. Herein, an optical tweezers‐based light‐driven technique is proposed to transport and construct multi‐core/shell microdroplets enveloping drug carriers and target cells as a novel, safe, and efficient targeted DDS. Drug‐loaded microdroplets with core/shell structures are first fabricated by a new and simple injection extrusion method and then it is transported to a target cell by a dynamic optical trap with a low optical power, which ensures no drug leakage, exogenous materials, and biothermal damage are introduced during the drug delivery process. Next, precise drug dosing is realized by pushing the target cell into the drug‐loaded microdroplet under the actions of optical forces, which constructs a multi‐core/shell microdroplet structure with a pure drug environment to improve the drug action efficiency. Moreover, quantitative drug dosing with a high drug utilization rate can also achieved by controlling the sizes/number of drug droplets inside the microdroplets. This light‐driven DDS is of great interest to frontier medical research and disease treatment fields.

Clusterization of Behavioral and Psychological Symptoms of Dementia as Assessed by Neuropsychiatric Inventory: A Case Against the Use of Principal Component Analysis.

The term Behavioral and Psychological Symptoms of Dementia (BPSD) covers a group of phenomenologically and medically distinct symptoms that rarely occur in isolation. Their therapy represents a major unmet medical need across dementias of different types, including Alzheimer's disease. Understanding of the symptom occurrence and their clusterization can inform clinical drug development and use of existing and future BPSD treatments. The primary aim of the present study was to investigate the ability of a commonly used principal component analysis to identify BPSD patterns as assessed by Neuropsychiatric Inventory (NPI). NPI scores from the Aging, Demographics, and Memory Study (ADAMS) were used to characterize reported occurrence of individual symptoms and their combinations. Based on this information, we have designed and conducted a simulation experiment to compare Principal Component analysis (PCA) and zero-inflated PCA (ZI PCA) by their ability to reveal true symptom associations. Exploratory analysis of the ADAMS database revealed overlapping multivariate distributions of NPI symptom scores. Simulation experiments have indicated that PCA and ZI PCA cannot handle data with multiple overlapping patterns. Although the principal component analysis approach is commonly applied to NPI scores, it is at risk to reveal BPSD clusters that are a statistical phenomenon rather than symptom associations occurring in clinical practice. We recommend the thorough characterization of multivariate distributions before subjecting any dataset to Principal Component Analysis.

Progress of medicinal plants and their active metabolites in ischemia-reperfusion injury of stroke: a novel therapeutic strategy based on regulation of crosstalk between mitophagy and ferroptosis.

The death of cells can occur through various pathways, including apoptosis, necroptosis, mitophagy, pyroptosis, endoplasmic reticulum stress, oxidative stress, ferroptosis, cuproptosis, and disulfide-driven necrosis. Increasing evidence suggests that mitophagy and ferroptosis play crucial regulatory roles in the development of stroke. In recent years, the incidence of stroke has been gradually increasing, posing a significant threat to human health. Hemorrhagic stroke accounts for only 15% of all strokes, while ischemic stroke is the predominant type, representing 85% of all stroke cases. Ischemic stroke refers to a clinical syndrome characterized by local ischemic-hypoxic necrosis of brain tissue due to various cerebrovascular disorders, leading to rapid onset of corresponding neurological deficits. Currently, specific therapeutic approaches targeting the pathophysiological mechanisms of ischemic brain tissue injury mainly include intravenous thrombolysis and endovascular intervention. Despite some clinical efficacy, these approaches inevitably lead to ischemia-reperfusion injury. Therefore, exploration of treatment options for ischemic stroke remains a challenging task. In light of this background, advancements in targeted therapy for cerebrovascular diseases through mitophagy and ferroptosis offer a new direction for the treatment of such diseases. In this review, we summarize the progress of mitophagy and ferroptosis in regulating ischemia-reperfusion injury in stroke and emphasize their potential molecular mechanisms in the pathogenesis. Importantly, we systematically elucidate the role of medicinal plants and their active metabolites in targeting mitophagy and ferroptosis in ischemia-reperfusion injury in stroke, providing new insights and perspectives for the clinical development of therapeutic drugs for these diseases.

Antimicrobial and antibiofilm effect of cannabinoids from Cannabis sativa against methicillin-resistant Staphylococcus aureus (MRSA) causing bovine mastitis.

Antimicrobial resistance (AMR) poses a serious threat to human, animal, and plant health on a global scale. Search and elimination techniques should be used to effectively counter the spread of methicillin-resistant Staphylococcus aureus (MRSA) infections. With only a few novel drugs in clinical development, the quest for plant-based alternatives to prevent the spread of antibiotic resistance among bacteria has accelerated. Treatment of MRSA infections is challenging owing to rapidly emerging resistance mechanisms coupled with their protective biofilms. In the present research, we examined the antibacterial properties of ten plant-derived ethanolic leaf extracts. The most effective ethanolic leaf extract against MRSA in decreasing order of zone of inhibition, Cannabis sativa L. > Syzygium cumini > Murraya koenigii > Eucalyptus sp. > while Aloe barbadensis, Azadirachta indica, had very little impact. Mangifera indica, Curcuma longa, Tinospora cordifolia, and Carica papaya did not exhibit inhibitory effects against MRSA; hence, Cannabis was selected for further experimental study. The minimal inhibitory concentration (MIC) of Cannabis sativa L. extract was 0.25mgml-1 with 86% mortality. At a sub-MIC dosage of 0.125mgml-1, the biofilm formation was reduced by 71%. The two major cannabinoids detected were cannabidiol and delta-9-tetrahydrocannabinol (Δ9-THC), which were majorly attributed to substantial inhibitory action against MRSA. The time-kill kinetics demonstrated a bactericidal action at 4 MIC over an 8-20-h time window with a 90% reduction in growth rate. The results from SEM, and light microscopy Giemsa staining revealed a reduction in cells in the treated group with increased AKP activity, indicating bacterial cell membrane breakdown. These findings suggested cannabinoids may be a promising alternative to antibiotic therapy for bovine biofilm-associated MRSA.

Mendelian randomization in eye disorders (review)

Background: Mendelian randomization (MR) is an analytic approach in genetic epidemiology which uses the results of genome-wide association studies (GWAS) to assess causal relationships between various risk factors and phenotypes of interest (or diseases) in ophthalmology. The aim of the study: To describe the current opportunities for the use of MR in ophthalmic research. Materials and methods: We searched Scopus, Medline and Web of Science journals up to August 2022 using the PRISMA protocol “Preferred Reporting Items for Systematic Review and Meta-Analysis”. The following information was extracted: identified risk factor(s) and disease(s), design of Mendelian randomization study, sensitivity analysis(es) performed if any, genetic instrument(s) used, and also whether the causal relationship between risk and outcome was confirmed by the results of the study. Results: The current review included a total of 37 publications that tested 211 associations of a risk factor with an ophthalmic disease. The articles included were published in peer-reviewed journals up to August 2022 with 25 of them (67,5%) published in 2020 or later. The MR method indicated the causal role of blood serum lipids, C-reactive protein, free thyroxine, smoking and alcohol intake in the development of age-related macular degeneration. For glaucoma, type 2 diabetes, serum glucose, refractive abnormalities, and elevated intraocular pressure have been shown to be significant. MR analysis revealed the role of elevated intraocular pressure, level of education, body weight at birth in the development of refractive errors. The role of type 2 diabetes, hypertension and smoking in the development of cataracts was determined using the MR method. Conclusion: The results of 97 MR analyses in ophthalmology have revealed a significant role of a number of risk factors in the development of eye diseases, which will serve as a guideline for clinical trials and drug development.

Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning

BackgroundDiscovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have been developed for DDI prediction. However, they generally require a huge number of samples, while known DDIs are rare.MethodsIn this work, we present KnowDDI, a graph neural network-based method that addresses the above challenge. KnowDDI enhances drug representations by adaptively leveraging rich neighborhood information from large biomedical knowledge graphs. Then, it learns a knowledge subgraph for each drug-pair to interpret the predicted DDI, where each of the edges is associated with a connection strength indicating the importance of a known DDI or resembling strength between a drug-pair whose connection is unknown. Thus, the lack of DDIs is implicitly compensated by the enriched drug representations and propagated drug similarities.ResultsHere we show the evaluation results of KnowDDI on two benchmark DDI datasets. Results show that KnowDDI obtains the state-of-the-art prediction performance with better interpretability. We also find that KnowDDI suffers less than existing works given a sparser knowledge graph. This indicates that the propagated drug similarities play a more important role in compensating for the lack of DDIs when the drug representations are less enriched.ConclusionsKnowDDI nicely combines the efficiency of deep learning techniques and the rich prior knowledge in biomedical knowledge graphs. As an original open-source tool, KnowDDI can help detect possible interactions in a broad range of relevant interaction prediction tasks, such as protein-protein interactions, drug-target interactions and disease-gene interactions, eventually promoting the development of biomedicine and healthcare.

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.

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.

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 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 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.