Drug Safety Evaluation Research Articles

Drug-Induced Mitochondrial Disruption in the Blood-Brain Barrier Cells: Overlooked Player in Drug Safety Evaluation.

The blood-brain barrier (BBB) is based on the unique pattern of the microvasculature of the central nervous system (CNS), which controls the transport of molecules between the CNS and the blood. The blood-brain barrier is mainly composed of endothelial cells, pericytes, and basement membrane, as well as the astrocytes and immune cells as perivascular macrophages and microglial cells. The dysfunction of this barrier can cause serious neuronal disorders due to the transport of hazardous molecules and immune cells to the CNS. Mitochondria plays a major role in cellular homeostasis in terms of health and disease. This review evaluated the published data about the effect of the drugs on the cells of BBB. Only seven articles were found that considered the effect of drugs on the barrier endothelial cells and mitochondria via different assays. Further studies are recommended to evaluate the impact of used medications on BBB cell bioenergetics. Also, the effect of the newly studied pharmaceutical agents on the BBB bioenergetics should be included within their safety profile studies.

Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach

Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceuticals. The need for precise mechanisms and clinical biomarkers to detect this adverse effect presents substantial challenges in drug safety assessment. In this study, we propose that analyzing the physicochemical properties of pharmaceuticals can provide valuable insights into their potential for torsadogenic cardiotoxicity. Our research centers on estimating TdP risk based on the molecular structure of drugs. We introduce a novel quantitative structure-toxicity relationship (QSTR) prediction model that leverages an in silico approach developed by adopting the 4R rule in laboratory animals. This approach eliminates the need for animal testing, saves time, and reduces cost. Our algorithm has successfully predicted the torsadogenic risks of various pharmaceutical compounds. To develop this model, we employed Support Vector Machine (SVM) and ensemble techniques, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). We enhanced the model's predictive accuracy through a rigorous two-step feature selection process. Furthermore, we utilized the SHapley Additive exPlanations (SHAP) technique to explain the prediction of torsadogenic risk, particularly within the RF model. This study represents a significant step towards creating a robust QSTR model, which can serve as an early screening tool for assessing the torsadogenic potential of pharmaceutical candidates or existing drugs. By incorporating molecular structure-based insights, we aim to enhance drug safety evaluation and minimize the risks of drug-induced TdP, ultimately benefiting both patients and the pharmaceutical industry.

Improved higher resolution cryo-EM structures reveal the binding modes of hERG channel inhibitors

During drug discovery, it is crucial to exclude compounds with toxic effects. The human ether-à-go-go-related gene (hERG) channel is essential for maintaining cardiac repolarization and is a critical target in drug safety evaluation due to its role in drug-induced arrhythmias. Inhibition of the hERG channel can lead to severe cardiac issues, including Torsades de Pointes tachycardia. Understanding hERG inhibition mechanisms is essential to avoid these toxicities. Several structural studies have elucidated the interactions between inhibitors and hERG. However, orientation and resolution issues have so far limited detailed insights. Here, we used digitonin to analyze the apo state of hERG, which resolved orientation issues and improved the resolution. We determined the structure of hERG bound to astemizole, showing a clear map in the pore pathway. Using this strategy, we also analyzed the binding modes of E-4031 and pimozide. These insights into inhibitor interactions with hERG may aid safer drug design and enhance cardiac safety.

Hepatotoxic assessment in a microphysiological system: Simulation of the drug absorption and toxic process after an overdosed acetaminophen on intestinal-liver-on-chip

To compensate the limitation of animal models, new models were proposed for drug safety evaluation to refine and reduce existing models. To mimic drug absorption and metabolism and predict toxicokinetic and toxic effects in an in vitro intestinal-liver microphysiological system (MPS), we constructed an intestinal-liver-on-chip and detected the acute liver injury process after an overdose of acetaminophen (APAP). Caco-2 and HT29-MTX-E12 cell lines were utilized to establish intestinal equivalents, along with HepG2, HUVEC-T1, and THP-1 induced by PMA and human hepatic stellate cell to establish liver equivalents. The APAP concentration was determined using high-performance liquid chromatography, and the toxicokinetic parameters were fitted using the non-compartmental analysis method by Phoenix. Changes in liver injury biomarkers aspartate aminotransferase and alanine aminotransferase, and liver function marker albumin indicated that the short-term culture of the two organs-on-chip model was stable for 4 days. Reactive oxygen species signaling was enhanced after APAP administration, along with decreased mitochondrial membrane potential, activated caspase-3, and enhanced p53 signaling, indicating a toxic response induced by APAP overdose. In the gut-liver MPS model, we fitted the toxicokinetic parameters and simulated the hepatotoxicity procedure following an APAP overdose, which will facilitate the organ-on-chips application in drug toxicity assays.

Long-Term Safety and Efficacy of Lenabasum, a Cannabinoid Receptor Type 2 Agonist, in Patients with Dermatomyositis with Refractory Skin Disease: Follow-Up Data from a 3-Year Open-Label Extension Study

BackgroundDermatomyositis (DM) is a rare autoimmune condition involving skin manifestations often resistant to standard treatments like immunosuppressants and antimalarials. Biopsies show elevated inflammatory cells such as CD4+ T cells, dendritic cells, and cytokines. Lenabasum, a selective CB2 agonist, has demonstrated significant benefits in treating autoimmune skin diseases. ObjectivesThis study utilizes data from the open-label extension (OLE) phase of the lenabasum phase 2 trial and additional post-OLE follow-up data. Key aims include evaluating the drug's long-term effectiveness and assessing disease manifestation recurrence. MethodsThe phase 2 lenabasum trial enrolled patients with treatment-resistant, skin-predominant DM. The OLE consisted of a 3-year period during which 20 patients were on the drug for the entire duration, with assessments every 8 weeks to evaluate drug safety and efficacy. Subsequently, a follow-up retrospective chart review was performed on patients who completed the OLE, as well as on control subjects with DM who did not participate in the lenabasum trial. ResultsBy week 68, patients exhibited reductions in CDASI activity score (-21.8), Patient Skin Activity VAS (-3.0), and Skindex-29 (-28.0) from OLE baseline. Post-OLE, 58.3% maintained stable disease, significantly higher than controls (p=0.035), with 41.7% not experiencing flares compared to 91.6% of controls. Additionally, 50% of patients reported sustained pruritus improvement. ConclusionsData from OLE and subsequent follow-up periods demonstrate lenabasum's efficacy in maintaining disease stability, reducing flares, and improving DM symptoms, suggesting it is a promising option for treatment-resistant skin-predominant DM patients.

Therapeutic Target Identification and Drug Discovery Driven by Chemical Proteomics.

Throughout the human lifespan, from conception to the end of life, small molecules have an intrinsic relationship with numerous physiological processes. The investigation into small-molecule targets holds significant implications for pharmacological discovery. The determination of the action sites of small molecules provide clarity into the pharmacodynamics and toxicological mechanisms of small-molecule drugs, assisting in the elucidation of drug off-target effects and resistance mechanisms. Consequently, innovative methods to study small-molecule targets have proliferated in recent years, with chemical proteomics standing out as a vanguard development in chemical biology in the post-genomic age. Chemical proteomics can non-selectively identify unknown targets of compounds within complex biological matrices, with both probe and non-probe modalities enabling effective target identification. This review attempts to summarize methods and illustrative examples of small-molecule target identification via chemical proteomics. It delves deeply into the interactions between small molecules and human biology to provide pivotal directions and strategies for the discovery and comprehension of novel pharmaceuticals, as well as to improve the evaluation of drug safety.

Use of Drug Sensitisers to Improve Therapeutic Index in Cancer.

The clinical management of malignant tumours is challenging, often leading to severe adverse effects and death. Drug resistance (DR) antagonises the effectiveness of treatments, and increasing drug dosage can worsen the therapeutic index (TI). Current efforts to overcome DR predominantly involve the use of drug combinations, including applying multiple anti-cancerous drugs, employing drug sensitisers, which are chemical agents that enhance pharmacokinetics (PK), including the targeting of cellular pathways and regulating pertinent membrane transporters. While combining multiple compounds may lead to drug-drug interactions (DDI) or polypharmacy effect, the use of drug sensitisers permits rapid attainment of effective treatment dosages at the disease site to prevent early DR and minimise side effects and will reduce the chance of DDI as lower drug doses are required. This review highlights the essential use of TI in evaluating drug dosage for cancer treatment and discusses the lack of a unified standard for TI within the field. Commonly used benefit-risk assessment criteria are summarised, and the critical exploration of the current use of TI in the pharmaceutical industrial sector is included. Specifically, this review leads to the discussion of drug sensitisers to facilitate improved ratios of effective dose to toxic dose directly in humans. The combination of drug and sensitiser molecules might see additional benefits to rekindle those drugs that failed late-stage clinical trials by the removal of detrimental off-target activities through the use of lower drug doses. Drug combinations and employing drug sensitisers are potential means to combat DR. The evolution of drug combinations and polypharmacy on TI are reviewed. Notably, the novel binary weapon approach is introduced as a new opportunity to improve TI. This review emphasises the urgent need for a criterion to systematically evaluate drug safety and efficiency for practical implementation in the field.

Reactive Species Risk Assessment Using Optimized HET-CAM Safety Evaluation of Feed Gas-Modified Gas Plasma Technology and Anticancer Drugs.

Clinical therapies, including dermatology and oncology, require safe application. In vitro experiments allow only limited conclusions about in vivo effects, while animal studies in, e.g., rodents have ethical constraints at a large scale. Chicken embryos lack pain reception until day 15 postfertilization, making the in ovo model a suitable alternative to in vivo safety assessment. In addition, the hen's egg test on chorioallantoic membrane assay allows irritation potential analysis for topical treatments, but standardized analysis has been limited so far. Medical gas plasma is a topical, routine, approved dermatology treatment. Recent work suggests the potential of this technology in oncology. Its main mode of action is the release of various reactive species simultaneously. Intriguingly, varying plasma feed gas compositions generates customized reactive species profiles previously shown to be optimized for specific applications, such as skin cancer treatment. To support clinical implications, we developed a novel chicken embryo CAM scoring and study scheme and employed the model to analyze 16 different plasma feed gas settings generated by the atmospheric pressure plasmajet kINPen, along with common anticancer drugs (e.g., cisplatin) and physiological mediators (e.g., VEGF). Extensive gas- and liquid-phase plasma reactive species profiling was done and was found to have a surprisingly low correlation with irritation potential parameters. Despite markedly different reactive species patterns, feed gas-modulated kINPen plasma was equally tolerated compared to standard argon plasma. CAM irritation with gas plasmas but not anticancer agents was reversed 48 h after treatment, underlining the only temporary tissue effects of medical gas plasma. Our results indicate a safe therapeutic application of reactive species.

Philosophical Interpretation of Article 24 of the Medicines Act: Drug Safety, Efficacy, and Quality Control

Article 24 of the Medicines Act serves as a cornerstone in the regulatory framework governing pharmaceuticals in the United Kingdom. This article emphasizes the rigorous evaluation of drug safety, efficacy, and quality through a structured, multi-phase process involving pre-clinical studies, clinical trials, and post-marketing surveillance. This paper explores the philosophical underpinnings of Article 24, delving into its ethical, epistemological, and regulatory dimensions. The ethical analysis focuses on balancing public health and individual rights, the principle of informed consent, and equitable access to high-quality medicines. The epistemological discussion highlights the commitment to evidence-based medicine, scientific rationalism, and the critical role of regulatory bodies like the Medicines and Healthcare products Regulatory Agency (MHRA). The regulatory perspective examines the comprehensive and precautionary approach to drug approval and quality control, emphasizing global harmonization and collaboration. By integrating these perspectives, the paper underscores the significance of Article 24 in ensuring that only safe, effective, and high-quality medicinal products are available in the UK, thereby setting a model for global regulatory practices.

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.

Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.

Efficacy and Safety of Cemiplimab for the Management of Non-Melanoma Skin Cancer: A Drug Safety Evaluation.

Non-melanoma skin cancer includes several types of cutaneous tumors, with basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) as the commonest. Among the available therapeutic options, surgical excision is the mainstay of treatment for both tumors. However, tumor features and patients' comorbidities may limit the use of these techniques, making the treatment challenging. As regards BCC, even if hedgehog inhibitors revolutionized the therapeutic scenario, there are still patients unresponsive or intolerant to these drugs. In this context, cemiplimab has been approved as second-line treatment. As regards SCC, cemiplimab was the first systemic therapy approved. The objective of this manuscript was to investigate the efficacy and safety of cemiplimab for the management of BCC and cSCC. Cemiplimab has a durable and significant effect for the management of BCC and CSCC, with a favorable safety profile. Different specialists including oncologists, radiologists, dermatologists, and surgeons are required to guarantee an integrated approach, leading to the best management of patients. Moreover, the collaboration among specialists will allow them to best manage the TEAEs, reducing the risk of treatment suspension or discontinuation. Certainly, ongoing studies and more and more emerging real-world evidence, will allow us to better characterize the role of cemiplimab for the management of advanced non-melanoma skin cancer.

Trophoblast stem cells, trophoblast invasion, and organoids - advancements in gynecology.

The human placenta serves as a vital barrier between the mother and the developing fetus during pregnancy. A defect in the early development of the placenta is associated with severe pregnancy disorders. Despite its complex development, various molecular processes control placental development, and the specialization of trophoblast cells is still not fully understood. One primary obstacle is the lack of suitable cell model systems. Traditional two-dimensional (2D) cell cultures fail to mimic in vivo conditions and do not capture the intricate intercellular interactions vital for studying placental development. However, three-dimensional (3D) organoid models derived from stem cells that replicate natural cell organization and architecture have greatly improved our understanding of trophoblast behavior and its medicinal applications. Organoids with relevant phenotypes provide a valuable platform to model both placental physiology and pathology, including the modeling of placental disorders. They hold great promise for personalized medicine, improved diagnostics, and the evaluation of pharmaceutical drug efficacy and safety. This article provides a concise overview of trophoblast stem cells, trophoblast invasion, and the evolving role of organoids in gynecology.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

Podophyllotoxin via SIRT1/PPAR /NF-κB axis induced cardiac injury in rats based on the toxicological evidence chain (TEC) concept

BackgroundThe study of cardiotoxicity of drugs has become an important part of clinical safety evaluation of drugs. It is commonly known that podophyllotoxin (PPT) and its many derivatives and congeners are broad-spectrum pharmacologically active substances. Clinical cardiotoxicity of PPT and its derivatives has been raised, basic research on the mechanism of cardiotoxicity remains insufficient. PurposeIn present study, our group's innovative concept of toxicological evidence chain (TEC) was applied to reveal the cardiac toxicity mechanism of PPT by targeted metabolomics, TMT-based quantitative proteomics and western blot. MethodsThe injury phenotype evidence (IPE) acquired from the toxicity manifestations, such as weight and behavior observation of Sprague-Dawley rat. The damage to rat hearts were assessed through histopathological examination and myocardial enzymes levels, which were defined as Adverse Outcomes Evidence (AOE). The damage to rat hearts was assessed through histopathological examination and myocardial enzyme levels, which were defined as evidence of adverse outcomes.Overall measurements of targeted metabolomics based on energy metabolism and TMT-based quantitative proteomics were obtained after exposure to PPT to acquire the Toxic Event Evidence (TEE). The mechanism of cardiac toxicity was speculated based on the integrated analysis of targeted metabolomics and TMT-based quantitative proteomics, which was verified by western blot. ResultsThe results indicated that exposure to PPT could result in significant elevation of myocardial enzymes and pathological alterations in rat hearts. In addition, we found that PPT caused disorders in cardiac energy metabolism, characterized by a decrease in energy metabolism fuels. TMT-based quantitative proteomics revealed that the PPAR (Peroxisome proliferators-activated receptor) signaling pathway needs further study. It is worth noting that PPT may suppress the expression of SIRT1, subsequently inhibiting AMPK, decreasing the expression of PGC-1α, PPARα and PPARγ. This results in disorders of glucose oxidation, glycolysis and ketone body metabolism. Additionally, the increase in the expression of p-IKK and p-IκBα, leads to the nuclear translocation of NF-κB p65 from the cytosol, thus triggering inflammation. ConclusionThis study comprehensively evaluated cardiac toxicity of PPT and initially revealed the mechanism of cardiotoxicity,suggesting that PPT induced disorders of energy metabolism and inflammation via SIRT1/PPAR/NF-κB axis, potentially contributing to cardiac injury.

Developing an AI-based prediction model for anaphylactic shock from injection drugs using Japanese real-world data and chemical structure-based analysis.

This study aims to develop an AI-based prediction model for injection drugs that cause anaphylactic shock using Japanese Real-World Data (JADER database) and chemical structure-based analysis. Data sourced from the JADER database included adverse drug reaction reports from April 2004 to December 2020. Only drugs with an adverse reaction named "anaphylactic shock" were selected for analysis. For model building, various models were constructed to predict anaphylactic shock-inducing drugs, such as logistic regression, LASSO, XGBoost, RF, SVM, and NNW. These models used chemical properties and structural similarities as feature variables. Dimension reduction was applied using principal component analysis. The dataset was split into training (80%) and validation (20%) sets. Six different models were trained and optimized through fivefold cross-validation. From April 2004 to December 2020, 947 drugs with the adverse reaction name "anaphylactic shock" were extracted from the JADER database. 320 drugs were excluded due to analytical challenges, and another 400 were removed due to their administration route. 227 drugs were finalized as target medicines. For model validation, the performance of each model was evaluated based on metrics like AUCs of ROC curve, sensitivity, and specificity. Additionally, two ensemble models, constructed from the six models were assessed using bootstrap sampling. Interestingly, it was identified that mepivacaine structural similarity had the highest importance in the final model. The study successfully developed an AI-based prediction model for anaphylactic shock inducing-injection drugs. The model would offer potential for drug safety evaluation and anaphylactic shock risk assessment.

Abstract 4192: In-depth evaluation of chemotherapy-induced mucositis in intestinal organoids using holotomography

Abstract Intestinal mucositis is a prevalent adverse event associated with antineoplastic therapies, such as chemotherapy, which still lacks a targeted and effective treatment regimen. The difficulty in preclinical prediction and prevention of drug-induced intestinal mucositis is attributed to the intricate underlying mechanisms of its onset and the broad spectrum of patient-specific responses to chemotherapeutic agents. Advancements in regenerative medicine have enabled the generation of intestinal organoids from adult stem cells, serving as a personalized model to emulate disease pathology and simulate individual drug responses. Various imaging techniques are employed to evaluate the responses of the organoids to pharmacological agents, but the inherent complexity of their three-dimensional (3D) architecture poses significant challenges in organoid imaging. These challenges are exacerbated by the standard preparation processes for imaging, including fixation and staining, which do not allow for the observation of dynamic, real-time effects of drugs on living organoids. In this study, we focused on meticulously examining the histopathological alterations in small intestinal organoids subjected to chemotherapeutic agents that induce mucositis, specifically 5-fluorouracil and irinotecan. Employing holotomography, a label-free real-time 3D imaging technology, we were able to visualize the destructive processes affecting the intestinal organoids, including the breakdown of the epithelial lining, the exfoliation of dead cells, and the alteration of the villus/crypt structure. Furthermore, we propose a novel methodology for quantifying the viability of intestinal organoids. This quantification is essential for assessing the extent of damage caused by the chemotherapeutic agents and for determining the maximum drug concentrations that can be deemed safe for use, thereby minimizing the risk of inducing mucositis while maintaining therapeutic efficacy. These results provide new insights into the toxic mechanisms of chemotherapeutic drugs and highlight the importance of using organoid models in drug safety evaluation throughout the development phase. Citation Format: Mahn Jae Lee, Jaehyeok Lee, Weisun Park, Geon Kim, Hye-Jin Kim, YongKeun Park. In-depth evaluation of chemotherapy-induced mucositis in intestinal organoids using holotomography [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 4192.

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.

Clinical practice guidelines for gene therapy to treat hereditary hearing loss

AbstractHereditary deafness is a common neurosensory disorder, and 148 non‐syndromic deafness genes have been identified to date. Gene therapy has been used to treat a variety of genetic diseases, but no gene therapy drug for hereditary deafness has been approved for clinical use. At present, several clinical trials of gene therapy for hereditary deafness are underway. However, few normative documents have been issued to guide the standardization of gene therapy for hearing loss, and this document is the first global gene therapy guideline for hereditary hearing loss. The guidelines were jointly developed and drafted by experienced audiologists, virologists and biologists who are vigorously involved in inner ear gene therapy research in the Hearing, Speech and Communication Subsociety of Biophysical Society of China, Audiology Development Foundation Of China and Audiology Subsociety of Jiangsu Medical Association. These guidelines cover preclinical research and clinical practice of gene therapy for hereditary deafness, including indications, key points of pre‐clinical research, patient selection criteria, pre‐clinical preparation, drug efficacy, drug safety evaluation criteria, ethical review, etc. We hope that the guidelines will promote the standardization of clinical practice related to gene therapy for hereditary deafness in China and around the world.

Drug Safety Evaluation of Sodium-Glucose Cotransporter 2 Inhibitors in Diabetic Comorbid Patients by Review of Systemic Extraglycemic Effects.

The aim of this study is to evaluate the safety of this drug in diabetic patients with comorbidities of all systems. In this review, the beneficial effects of this drug and its mechanism on the disorders of every system of humans in relation to diabetes have been studied, and finally, its adverse effects have also been discussed. The search for relevant information is carried out in the PubMed and Google Scholar databases by using the following terms: diabetes mellitus type 2, SGLT, SGLT2 inhibitors, (SGLT2 inhibitors) AND (Pleiotropic effects). All English-published articles from 2016 to 2023 have been used in this study. It should be noted that a small number of articles published before 2016 have been used in the introduction and general informations. Its beneficial effects on improving cardiovascular disease risk factors and reducing adverse events caused by cardiovascular and renal diseases have proven in most large clinical studies that these effects are almost certain. It also has beneficial effects on other human systems such as the respiratory system, the gastrointestinal system, the circulatory system, and the nervous system; more of them are at the level of clinical and pre-clinical trials but have not been proven in large clinical trials or meta-analyses. With the exception of a few adverse effects, this drug is considered a good choice and safe for all diabetic patients with comorbidities of all systems.

A case-control study on the clinical characteristics of granisetron-related arrhythmias and the development of a predictive nomogram.

Automatic monitoring and assessment are increasingly employed in drug safety evaluations using hospital information system data. The increasing concern about granisetron-related arrhythmias requires real-world studies to improve our understanding of its safety. This study aimed to analyze the incidence, clinical characteristics, and risk factors of granisetron-related arrhythmias in hospitalized patients using real-world data obtained from the Adverse Drug Event Active Surveillance and Assessment System-II (ADE-ASAS-II) and concurrently aimed to develop and validate a nomogram to predict the occurrence of arrhythmias. Retrospective automatic monitoring of inpatients using granisetron was conducted in a Chinese hospital from January 1, 2017, to December 31, 2021, to determine the incidence of arrhythmias using ADE-ASAS- II. Propensity score matching was used to balance confounders and analyze clinical characteristics. Based on risk factors identified through logistic regression analysis, a prediction nomogram was established and internally validated using the Bootstrap method. Arrhythmias occurred in 178 of 72,508 cases taking granisetron with an incidence of 0.3%. Independent risk factors for granisetron-related arrhythmias included medication duration, comorbid cardiovascular disease, concomitant use of other 5-hydroxytryptamine 3 receptor antagonists, alanine aminotransferase > 40 U/L, and blood urea nitrogen > 7.5mmol/L. The nomogram demonstrated good differentiation and calibration, with enhanced clinical benefit observed when the risk threshold ranged from 0.10 to 0.82. The nomogram, based on the five identified independent risk factors, may be valuable in predicting the risk of granisetron-related arrhythmias in the administered population, offering significant clinical applications.