Drug Safety Assessment Research Articles

Digitalization of toxicology: improving preclinical to clinical translation.

Though the portfolio of medicines that are extending and improving the lives of patients continues to grow, drug discovery and development remains a challenging business on its best day. Safety liabilities are a significant contributor to development attrition where the costliest liabilities to both drug developers and patients emerge in late development or post-marketing. Animal studies are an important and influential contributor to the current drug discovery and development paradigm intending to provide evidence that a novel drug candidate can be used safely and effectively in human volunteers and patients. However, translational gaps-such as toxicity in patients not predicted by animal studies-have prompted efforts to improve their effectiveness, especially in safety assessment. More holistic monitoring and "digitalization" of animal studies has the potential to enrich study outcomes leading to datasets that are more computationally accessible, translationally relevant, replicable, and technically efficient. Continuous monitoring of animal behavior and physiology enables longitudinal assessment of drug effects, detection of effects during the animal's sleep and wake cycles and the opportunity to detect health or welfare events earlier. Automated measures can also mitigate human biases and reduce subjectivity. Reinventing a conservative, standardized, and traditional paradigm like drug safety assessment requires the collaboration and contributions of a broad and multi-disciplinary stakeholder group. In this perspective, we review the current state of the field and discuss opportunities to improve current approaches by more fully leveraging the power of sensor technologies, artificial intelligence (AI), and animal behavior in a home cage environment.

Standard Operating Procedure to Optimize Resazurin-Based Viability Assays

The resazurin assay, also known as the Alamar Blue assay, stands as a cornerstone technique in cell biology, microbiology, and drug development. It assesses the viability of cells through the conversion of resazurin into highly fluorescent resorufin. The resulting fluorescence intensity provides a reliable estimate of viable cell numbers. Cytotoxicity assays, such as the resazurin-based method, play a crucial role in the screening of potential drug candidates and in the assessment of pharmaceutical and chemical toxicity. In recent years, inconsistencies have arisen in pharmacogenomic studies, often due to poorly optimized laboratory protocols. These inconsistencies hinder progress in understanding how substances affect cell health, leading to unreliable findings. Thus, the need for standardized and rigorously optimized protocols is evident to ensure consistent and accurate results in cytotoxicity studies. This manuscript describes a standardized procedure for optimizing resazurin-based viability assays to improve the reliability of cytotoxicity data. This optimization approach focuses on critical experimental parameters and data quality, aiming to achieve a level of measurement imprecision of less than 20%. In conclusion, to address the critical issues of reproducibility and reliability, protocol standardization, such as the one described in this manuscript, can greatly enhance the credibility of cytotoxicity studies, ultimately advancing drug safety assessments.

Challenging the status quo: a framework for mechanistic and human-relevant cardiovascular safety screening.

Traditional approaches to preclinical drug safety assessment have generally protected human patients from unintended adverse effects. However, these assessments typically occur too late to make changes in the formulation or in phase 1 and beyond, are highly dependent on animal studies and have the potential to lead to the termination of useful drugs due to liabilities in animals that are not applicable in patients. Collectively, these elements come at great detriment to both patients and the drug development sector. This phenomenon is particularly problematic in the area of cardiovascular safety assessment where preclinical attrition is high. We believe that a more efficient and translational approach can be defined. A multi-tiered assessment that leverages our understanding of human cardiovascular biology, applies human cell-based in vitro characterizations of cardiovascular responses to insult, and incorporates computational models of pharmacokinetic relationships would enable earlier and more translational identification of human-relevant liabilities. While this will take time to develop, the ultimate goal would be to implement such assays both in the lead selection phase as well as through regulatory phases.

In silico off-target profiling for enhanced drug safety assessment

Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent phases. However, the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is substantial. Drug off-target interactions, along with the adverse drug reactions they induce, are significant factors affecting drug safety. To assess the liability of candidate drugs, we developed an artificial intelligence model for the precise prediction of compound off-target interactions, leveraging multi-task graph neural networks. The outcomes of off-target predictions can serve as representations for compounds, enabling the differentiation of drugs under various ATC codes and the classification of compound toxicity. Furthermore, the predicted off-target profiles are employed in adverse drug reaction (ADR) enrichment analysis, facilitating the inference of potential ADRs for a drug. Using the withdrawn drug Pergolide as an example, we elucidate the mechanisms underlying ADRs at the target level, contributing to the exploration of the potential clinical relevance of newly predicted off-target interactions. Overall, our work facilitates the early assessment of compound safety/toxicity based on off-target identification, deduces potential ADRs of drugs, and ultimately promotes the secure development of drugs.

Estimation and reduction of bias in self-controlled case series with non-rare event dependent outcomes and heterogeneous populations.

The self-controlled case series (SCCS) is a commonly adopted study design in the assessment of vaccine and drug safety. Recurrent event data collected from SCCS studies are typically analyzed using the conditional Poisson model which assumes event times are independent within-cases. This assumption is violated in the presence of event dependence, where the occurrence of an event influences the probability and timing of subsequent events. When event dependence is suspected in an SCCS study, the standard recommendation is to include only the first event from each case in the analysis. However, first event analysis can still yield biased estimates of the exposure relative incidence if the outcome event is not rare. We first demonstrate that the bias in first event analysis can be even higher than previously assumed when subpopulations with different baseline incidence rates are present and describe an improved method for estimating this bias. Subsequently, we propose a novel partitioned analysis method and demonstrate how it can reduce this bias. We provide a recommendation to guide the number of partitions to use with the partitioned analysis, illustrate this recommendation with an example SCCS study of the association between beta-blockers and acute myocardial infarction, and compare the partitioned analysis against other SCCS analysis methods by simulation.

Safety Profile of Paxlovid in the Treatment of COVID-19.

With the urgent and widespread application of Paxlovid, a novel antiviral drug for Coronavirus Disease 2019 (COVID-19) in clinical practice, concerns regarding its actual efficacy and safety have emerged. In order to provide more evidence to support its clinical application, we sought to perform a descriptive analysis of cases who experienced at least one Paxlovid-related adverse event (AEs) and reported to the FDA Adverse Event Reporting System (FAERS) in the post-marketing period. Individual adverse event reports between January 1, 2022 and September 30, 2022, were downloaded from the FAERS website. We completed a descriptive study about the safety of Paxlovid in the treatment of COVID-19. Further, we also analyzed the onset time of Paxlovid-related AEs. As of 30 September 2022, 16,529 de-duplicated cases were submitted to the FDA, and 5,860 (35.45%) were female. The average age was 58.38 years (S.D. 15.50). Most reports (12,390, 74.96%) were submitted by consumers and 1,436 (8.68%) concerned serious outcomes. The most frequently reported AEs were disease recurrence (7,724, 16.23%), dysgeusia (2,877, 6.05%), and diarrhoea (1,448, 3.04%). The median onset time of Paxlovid-related AEs was 8 days (interquartile range,1-10 days), and most of the cases (2,629, 19.12%) occurred on the day after Paxlovid initiation. This study indicates that the most common AEs reported with Paxlovid in post-marketing experience are consistent with the safety assessment of antiviral drugs. Even without emerging apparent safety concerns, the incidence of serious outcomes was unexpectedly high, and a few cases of potential new AEs occurred.

Standardizing to specific target populations in distributed networks and multisite pharmacoepidemiologic studies.

Distributed network studies and multisite studies assess drug safety and effectiveness in diverse populations by pooling information. Targeting groups of clinical or policy interest (including specific sites or site combinations) and applying weights based on effect measure modifiers (EMMs) prior to pooling estimates within multisite studies may increase interpretability and improve precision. We simulated a 4-site study, standardized each site using inverse odds weights (IOWs) to resemble the 3 smallest sites or the smallest site, estimated IOW-weighted risk differences (RDs), and combined estimates with inverse variance weights (IVWs). We also created an artificial distributed network in the Clinical Practice Research Datalink (CPRD) Aurum consisting of 1 site for each geographic region. We compared metformin and sulfonylurea initiators with respect to mortality, targeting the smallest region. In the simulation, IOWs reduced differences between estimates and increased precision when targeting the 3 smallest sites or the smallest site. In the CPRD Aurum study, the IOW+IVW estimate was also more precise (smallest region: RD = 5.41% [95% CI, 1.03-9.79]; IOW+IVW estimate: RD = 3.25% [95% CI, 3.07-3.43]). When performing pharmacoepidemiologic research in distributed networks or multisite studies in the presence of EMMs, designation of target populations has the potential to improve estimate precision and interpretability. This article is part of a Special Collection on Pharmacoepidemiology.

In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods.

The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.

Efficacy and safety of tongxinluo capsule for angina pectoris of coronary heart disease: an overview of systematic reviews and meta-analysis.

Tongxinluo capsule (TXLC) is a common drug for treating angina pectoris of coronary heart disease (CHD). In recent years, many systematic reviews (SRs) and meta-analyses (MAs) have reported the efficacy and safety of TXLC for improving angina symptoms in patients with CHD. We aimed to comprehensively evaluate the existing SRs and MAs of TXLC in treating angina pectoris of CHD, summarize the evidence quality, and provide scientific evidence and recommendations. We searched seven databases for relevant SRs/MAs published up to 1 June 2023. Two reviewers independently completed the literature retrieval, screening, and data extraction. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) to evaluate the methodological quality, the Risk of Bias in Systematic Reviews (ROBIS) to assess the risk of bias, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to determine the strength of the evidence. RevMan 5.3 was used to synthesize data. We identified 15 SRs/MAs, including 329 RCTs and 33,417 patients. According to the evaluation results of AMSTAR-2, only one SR was of high methodological quality, the others were very low. ROBIS assessment showed that one SR (6.67%) had a low risk, 3 SRs (20%) had an unclear risk, and 11 SRs (73.33%) had a high risk. We assessed 42 outcomes by the GRADE, 10 (23.81%) for moderate-quality evidence, 17 (40.48%) for low-quality evidence, and 15 (35.71%) for very-low-quality evidence. Mate-analysis showed that TXLC combined with conventional western medications improved electrocardiogram efficacy (RR = 1.38, 95% CI: 1.23-1.43, P < 0.001) and angina efficacy (OR = 3.58, 95% CI: 3.02-4.24, P < 0.001), reduced angina attack frequency (SMD = -0.54, 95% CI: -0.64 to -0.44, P < 0.001) and angina duration (SMD = -0.42, 95% CI: -0.57 to -0.28, P < 0.001), with general heterogeneity. The pooled results showed that TXLC appears to have some efficacy in improving cardiac function and relieving angina symptoms, but there is limited evidence that it improves cardiovascular event rates, hemorheology, lipids, or hs-CRP. In the assessment of drug safety, TXLC was associated with different degrees of adverse drug reactions. Based on the evidence, TXLC may be effective as an adjuvant treatment for angina pectoris of CHD. However, the quality of the evidence is low, and the drug's safety must be carefully interpreted. In future studies, high-quality randomized controlled trials are needed to confirm the effectiveness and safety of TXLC. http://www.crd.york.ac.uk/PROSPERO/, identifier (CRD42022365372).

Evaluating ChatGPT's efficacy in assessing the safety of non-prescription medications and supplements in patients with kidney disease.

This study investigated the efficacy of ChatGPT-3.5 and ChatGPT-4 in assessing drug safety for patients with kidney diseases, comparing their performance to Micromedex, a well-established drug information source. Despite the perception of non-prescription medications and supplements as safe, risks exist, especially for those with kidney issues. The study's goal was to evaluate ChatGPT's versions for their potential in clinical decision-making regarding kidney disease patients. The research involved analyzing 124 common non-prescription medications and supplements using ChatGPT-3.5 and ChatGPT-4 with queries about their safety for people with kidney disease. The AI responses were categorized as "generally safe," "potentially harmful," or "unknown toxicity." Simultaneously, these medications and supplements were assessed in Micromedex using similar categories, allowing for a comparison of the concordance between the two resources. Micromedex identified 85 (68.5%) medications as generally safe, 35 (28.2%) as potentially harmful, and 4 (3.2%) of unknown toxicity. ChatGPT-3.5 identified 89 (71.8%) as generally safe, 11 (8.9%) as potentially harmful, and 24 (19.3%) of unknown toxicity. GPT-4 identified 82 (66.1%) as generally safe, 29 (23.4%) as potentially harmful, and 13 (10.5%) of unknown toxicity. The overall agreement between Micromedex and ChatGPT-3.5 was 64.5% and ChatGPT-4 demonstrated a higher agreement at 81.4%. Notably, ChatGPT-3.5's suboptimal performance was primarily influenced by a lower concordance rate among supplements, standing at 60.3%. This discrepancy could be attributed to the limited data on supplements within ChatGPT-3.5, with supplements constituting 80% of medications identified as unknown. ChatGPT's capabilities in evaluating the safety of non-prescription drugs and supplements for kidney disease patients are modest compared to established drug information resources. Neither ChatGPT-3.5 nor ChatGPT-4 can be currently recommended as reliable drug information sources for this demographic. The results highlight the need for further improvements in the model's accuracy and reliability in the medical domain.

Metabolomics in Preclinical Drug Safety Assessment: Current Status and Future Trends.

Metabolomics is emerging as a powerful systems biology approach for improving preclinical drug safety assessment. This review discusses current applications and future trends of metabolomics in toxicology and drug development. Metabolomics can elucidate adverse outcome pathways by detecting endogenous biochemical alterations underlying toxicity mechanisms. Furthermore, metabolomics enables better characterization of human environmental exposures and their influence on disease pathogenesis. Metabolomics approaches are being increasingly incorporated into toxicology studies and safety pharmacology evaluations to gain mechanistic insights and identify early biomarkers of toxicity. However, realizing the full potential of metabolomics in regulatory decision making requires a robust demonstration of reliability through quality assurance practices, reference materials, and interlaboratory studies. Overall, metabolomics shows great promise in strengthening the mechanistic understanding of toxicity, enhancing routine safety screening, and transforming exposure and risk assessment paradigms. Integration of metabolomics with computational, in vitro, and personalized medicine innovations will shape future applications in predictive toxicology.

Multivariate generalized mixed-effects models for screening multiple adverse drug reactions in spontaneous reporting systems.

Introduction: For assessing drug safety using spontaneous reporting system databases, quantitative measurements, such as proportional reporting rate (PRR) and reporting odds ratio (ROR), are widely employed to assess the relationship between a drug and a suspected adverse drug reaction (ADR). The databases contain numerous ADRs, and the quantitative measurements need to be calculated by performing the analysis multiple times for each ADR. We proposed a novel, simple, and easy-to-implement method to estimate the PRR and ROR of multiple ADRs in a single analysis using a generalized mixed-effects model for signal detection. Methods: The proposed method simultaneously analyzed the association between any drug and numerous ADRs, as well as estimated the PRR and ROR for a specific combination of drugs and suspected ADRs. Furthermore, the proposed method was applied to detect drug-drug interactions associated with the concurrent use of two or more drugs. Results and discussion: In our simulation studies, the false-positive rate and sensitivity of the proposed method were similar to those of the traditional PRR and ROR. The proposed method detected known ADRs when applied to the Food and Drug Administration Adverse Event Reporting System database. As an important advantage, the proposed method allowed the simultaneous evaluation of several ADRs using multiple drugs.

Application and interpretation of immunophenotyping data in safety and risk assessment.

The use of immunophenotyping during immunotoxicity investigations was first popularized in the 1980s and has since become more integrated into diagnostic and non-clinical assessments. The data provided from immunophenotyping can serve as an initial source of information to guide decisions for additional, more advanced, immunotoxicity testing as well as for human health safety and risk assessment of drugs and chemicals. However, comprehensive guidance describing applications of immunophenotyping data in immunotoxicity investigations is lacking, particularly among regulatory bodies. Therefore, a critical examination is needed for the appropriate interpretations and potential misinterpretations of such data during the assessment of drug safety and chemical risk. As such, the current uses and implications of immunophenotyping data in human health safety and risk assessments has been evaluated to provide additional context for the application of current methodologies and guidelines. In addition, case studies are presented to highlight the challenges of interpreting immunophenotyping results along with incorporating the findings into immunotoxicity investigations. Based on the analyses of current approaches and methodologies, a decision flow is presented for use of immunophenotyping data during risk informed decision making.

Roles of Community Pharmacists in Screening and Disseminating of Information about Non-Steroidal Anti-Inflammatory Drugs Risks: Implications for Drug Safety Assessment

Roles of Community Pharmacists in Screening and Disseminating of Information about Non-Steroidal Anti-Inflammatory Drugs Risks: Implications for Drug Safety Assessment

Extending the shelf life of HLM chips through freeze-drying of human liver microsomes immobilized onto thiol-ene micropillar arrays.

Microfluidic flow reactors functionalized with immobilized human liver microsomes (HLM chips) represent a powerful tool for drug discovery and development by enabling mechanism-based enzyme inhibition studies under flow-through conditions. Additionally, HLM chips may be exploited in streamlined production of human drug metabolites for subsequent microfluidic in vitro organ models or as metabolite standards for drug safety assessment. However, the limited shelf life of the biofunctionalized microreactors generally poses a major barrier to their commercial adaptation in terms of both storage and shipping. The shelf life of the HLM chips in the wetted state is ca. 2-3 weeks only and requires cold storage at 4 °C. In this study, we developed a freeze-drying method for lyophilization of HLMs that are readily immobilized inside microfluidic pillar arrays made from off-stoichiometric thiol-ene polymer. The success of lyophilization was evaluated by monitoring the cytochrome P450 and UDP-glucuronosyltransferase enzyme activities of rehydrated HLMs for several months post-freeze-drying. By adapting the freeze-drying protocol, the HLM chips could be stored at room temperature (protected from light and moisture) for at least 9 months (n = 2 independent batches) and up to 16 months at best, with recovered enzyme activities within 60-120% of the non-freeze-dried control chips. This is a major improvement over the cold-storage requirement and the limited shelf life of the non-freeze-dried HLM chips, which can significantly ease the design of experiments, decrease energy consumption during storage, and reduce the shipping costs with a view to commercial adaptation.

Meta-analysis of Censored Adverse Events

Meta-analysis is a powerful tool for assessing drug safety by combining treatment-related toxicological findings across multiple studies, as clinical trials are typically underpowered for detecting adverse drug effects. However, incomplete reporting of adverse events (AEs) in published clinical studies is frequently encountered, especially if the observed number of AEs is below a pre-specified study-dependent threshold. Ignoring the censored AE information, often found in lower frequency, can significantly bias the estimated incidence rate of AEs. Despite its importance, this prevalent issue in meta-analysis has received little statistical or analytic attention in the literature. To address this challenge, we propose a Bayesian approach to accommodating the censored and possibly rare AEs for meta-analysis of safety data. Through simulation studies, we demonstrate that the proposed method can improve accuracy in point and interval estimation of incidence probabilities, particularly in the presence of censored data. Overall, the proposed method provides a practical solution that can facilitate better-informed decisions regarding drug safety.

Adverse Drug Reactions: Identification, Prevention and Management

This article addresses the critical aspects of adverse drug reactions (ADRs), focusing on their identification, prevention, and management. ADRs, defined as harmful or unintended reactions to drugs, necessitate a nuanced understanding due to their varying severity. Side effects, though not always harmful, can evolve into ADRs, prompting intervention. The decision-making process involves assessing the adverse reaction's severity, potential benefits of the drug, and the overall risk-benefit profile for the patient. Healthcare professionals play a pivotal role in monitoring and reporting ADRs to regulatory authorities for ongoing drug safety assessment. The article further discusses the classification of ADRs and outlines strategies for identification, including clinical manifestations, patient reporting, and pharmacovigilance systems. Management strategies, particularly in the case of anaphylaxis, underscore the importance of early recognition, acute therapy, and drug discontinuation. The prevention of ADRs involves a comprehensive approach, encompassing patient assessment, communication among healthcare providers, education, and the use of electronic health records. The collaborative effort between stakeholders and active participation in pharmacovigilance programs contribute to ongoing drug safety enhancement. The article concludes by emphasizing the collective goal of minimizing ADR occurrence and impact to enhance overall pharmacotherapy safety and effectiveness.

A (not) new Rx: Quality Improvement for NAM

New Approach Methodologies (NAMs) are a rapidly growing set of tools/methods for use food, drug, consumer product, or chemical safety assessment paradigms. The massive growth in NAMs tech development, publication, and legislation has been paralleled by a growing sense of frustration. The challenge of realizing the systems-level changes needed to catalyze the broad-scale adoption and use of NAMs is substantial. This Commentary asserts that these challenges may be less unique than perceived to date, and points to specific opportunities to learn from decades of experience (both positive and negative) from the Quality Improvement (QI) movement in the public health and healthcare arenas. Specific recommendations to inform and guide NAMs development are offered.

Enabling novel paradigms: a biological questions-based approach to human chemical hazard and drug safety assessment.

Throughput needs, costs of time and resources, and concerns about the use of animals in hazard and safety assessment studies are fueling a growing interest in adopting new approach methodologies for use in product development and risk assessment. However, current efforts to define "next-generation risk assessment" vary considerably across commercial and regulatory sectors, and an a priori definition of the biological scope of data needed to assess hazards is generally lacking. We propose that the absence of clearly defined questions that can be answered during hazard assessment is the primary barrier to the generation of a paradigm flexible enough to be used across varying product development and approval decision contexts. Herein, we propose a biological questions-based approach (BQBA) for hazard and safety assessment to facilitate fit-for-purpose method selection and more efficient evidence-based decision-making. The key pillars of this novel approach are bioavailability, bioactivity, adversity, and susceptibility. This BQBA is compared with current hazard approaches and is applied in scenarios of varying pathobiological understanding and/or regulatory testing requirements. To further define the paradigm and key questions that allow better prediction and characterization of human health hazard, a multidisciplinary collaboration among stakeholder groups should be initiated.

The Critical Intersect of Regulations, Health Technology Assessment, and Drug Safety Assessments.

Health technology assessment (HTA) is a multidisciplinary process that determines the value of health technology at different points in its lifecycle. Safety issues have become more important since regulatory authorities are increasingly adopting flexible standards, processes, and evidentiary requirements for drug approval. In this article, we compared the different role of regulatory authorities and HTA agencies. Additionally, the experience of regulatory-HTA collaboration for assessment and/or decision-making on safety issues in the lifecycle of a health technology is illustrated, including olmesartan (angiotensin II receptor antagonist) and the direct-acting hepatitis C virus (HCV) antiviral agents. Post-licensing data can be derived from various sources such as electronic health records, medical claims, drug and disease registries, post-authorization safety studies (PASS) or post-authorization safety efficacy studies (PAES), periodic benefit-risk assessment reports, as well as HTA reassessment reports, which incorporate utilization information from patients in a real-world setting and provide crucial evidence for various purposes. With the ongoing accumulation of safety and efficacy information during post-regulatory approval, a standardized process for continuous data collection and active reassessment of risk and benefit becomes crucial for managing the lifecycle of health technologies. In order to define evidence requirements clearly, reduce uncertainty, and minimize delays in HTA approval, early engagement and collaboration of HTA agencies in the regulatory review processes have become more common. However, there is currently limited interaction and collaboration between regulatory authorities and HTA agencies. This article aims to identify the challenges faced by regulators and HTA agencies today, emphasizing the significance of conducting regulatory reviews and health technology assessments throughout a technology's lifecycle, underlining the value of utilizing real-world data and evidence, and emphasizing the necessity of enhancing collaboration between regulatory authorities and HTA agencies, all within the overarching context of drug safety.