Translatability of preclinical to early clinical tolerable and pharmacologically active dose ranges for central nervous system active drugs.

The primary purpose of this study was to assess the translatability of preclinical to early clinical tolerable and pharmacologically active dose ranges for central nervous system (CNS) active drugs. As a part of this, IBs were reviewed on reporting quality. Investigator's Brochures (IBs) of studies performed at the Centre for Human Drug Research (CHDR) reporting statistically significant results of CNS activity related to the drug's mechanism of action were included. The quality of IBs was assessed based on the presence of a rationale for the chosen animal model, completeness of pharmacokinetic (PK) results in reporting and internal validity information of the preclinical evidence. The IB-derisk tool was used to generate preclinical and early clinical data overviews data. For each compound, the overlap between pharmacologically active dose ranges and well-tolerated levels was calculated for three pharmacokinetic (PK) parameters: human equivalent dose (HED), maximum plasma concentration (Cmax) and area under the curve (AUC). Twenty-five IBs were included. In general, the quality of reporting in IBs was assessed as poor. About a third of studies did not explore the entire concentration-effect curve (pre)clinically. Single dose tolerability ranges were most accurately predicted by Cmax. Human equivalent dose and AUC were the best predictors of pharmacologically active ranges. Tolerable and pharmacologically active dose ranges in healthy volunteers can be reasonably well predicted from preclinical data with the IB-derisk tool. The translatability of preclinical studies can be improved by applying a higher reporting standard in IBs including comparable PK measurements across all preclinical and clinical studies.

Sex Proportionality in Pre-clinical and Clinical Trials: An Evaluation of 22 Marketing Authorization Application Dossiers Submitted to the European Medicines Agency.

This study assessed to what extent women were included in all phases of drug development; whether the clinical studies in the marketing authorization application dossiers include information per sex; and explored whether there are differences between women and men in the drugs' efficacy and safety. Data were extracted from dossiers submitted to the European Medicines Agency. Twenty-two dossiers of drugs approved between 2011 and 2015 for the treatment of various diseases were included. Female animals were included in only 9% of the pharmacodynamics studies, but female and male animals were included in all toxicology studies. Although fewer women than men were included in the clinical studies used to evaluate pharmacokinetics (PK) (29 to 40% women), all dossiers contained sex-specific PK parameter estimations. In the phase III trials, inclusion of women was proportional to disease prevalence for depression, epilepsy, thrombosis, and diabetes [participation to prevalence ratio (PPR) range: 0.91-1.04], but women were considered underrepresented for schizophrenia, hepatitis C, hypercholesterolemia, HIV, and heart failure (PPR range: 0.49-0.74). All dossiers contained sex-specific subgroup analyses of efficacy and safety. There seemed to be higher efficacy for women in one dossier and a trend toward lower efficacy in another dossier. More women had adverse events in both treatment (73.0 vs. 70.6%, p < 0.001) and placebo groups (69.5 vs. 65.5%, p < 0.001). In conclusion, women were included throughout all phases of clinical drug research, and sex-specific information was available in the evaluated dossiers. The included number of women was, however, not always proportional to disease prevalence rates.

Tradition, not science, is the basis of animal model selection in translational and applied research.

National and international laws and regulations exist to protect animals used for scientific purposes in translational and applied research, which includes drug development. However, multiple animal models are available for each disease. We evaluated the argumentation behind the selection of a specific animal model using thematic content analysis in project applications issued in 2017-2019 in the Netherlands. In total, 125 animal models for translational and applied research from 110 project applications were assessed. Explanations to select a specific model included: the model's availability (79%); the availability of expertise (62%); and the model showing similar disease pathology/symptoms (59%) to humans. Therefore, current selection of a specific animal model seems to be based on tradition rather than its potential predictive value for clinical outcome. The applicants' explanations for the implementation of the 3R prin­ciples (replacement, reduction and refinement) as to the animal model were unspecific. Replacement was achieved by using data from prior in vitro studies, reduction by optimal experimental design and statistics, and refinement by reducing discomfort. Additionally, due to the stated need for a test model with high complexity (47%) and intactness (30%), the full replacement of animal models with alternative (non-live animal) approaches was thought unachievable. Without a clear, systematic and transparent justification for the selection of a specific animal model, the likelihood of poorly trans­latable research remains. It is not only up to the researcher to demonstrate this, as ethical committees and funding bodies can provide positive stimuli to drive this change.

Levelling the Translational Gap for Animal to Human Efficacy Data.

Reports of a reproducibility crisis combined with a high attrition rate in the pharmaceutical industry have put animal research increasingly under scrutiny in the past decade. Many researchers and the general public now question whether there is still a justification for conducting animal studies. While criticism of the current modus operandi in preclinical research is certainly warranted, the data on which these discussions are based are often unreliable. Several initiatives to address the internal validity and reporting quality of animal studies (e.g., Animals in Research: Reporting In Vivo Experiments (ARRIVE) and Planning Research and Experimental Procedures on Animals: Recommendations for Excellence (PREPARE) guidelines) have been introduced but seldom implemented. As for external validity, progress has been virtually absent. Nonetheless, the selection of optimal animal models of disease may prevent the conducting of clinical trials, based on unreliable preclinical data. Here, we discuss three contributions to tackle the evaluation of the predictive value of animal models of disease themselves. First, we developed the Framework to Identify Models of Disease (FIMD), the first step to standardise the assessment, validation and comparison of disease models. FIMD allows the identification of which aspects of the human disease are replicated in the animals, facilitating the selection of disease models more likely to predict human response. Second, we show an example of how systematic reviews and meta-analyses can provide another strategy to discriminate between disease models quantitatively. Third, we explore whether external validity is a factor in animal model selection in the Investigator's Brochure (IB), and we use the IB-derisk tool to integrate preclinical pharmacokinetic and pharmacodynamic data in early clinical development. Through these contributions, we show how we can address external validity to evaluate the translatability and scientific value of animal models in drug development. However, while these methods have potential, it is the extent of their adoption by the scientific community that will define their impact. By promoting and adopting high quality study design and reporting, as well as a thorough assessment of the translatability of drug efficacy of animal models of disease, we will have robust data to challenge and improve the current animal research paradigm.

Comparison of drug efficacy in two animal models of type 2 diabetes: A systematic review and meta-analysis.

Previous qualitative research has suggested there are only minor differences between the db/db mouse and the Zucker Diabetic Fatty (ZDF) rat, both animal models of type 2 diabetes. However, it is not known whether these models are also comparable regarding drug response in quantitative terms (effect size). To investigate the extent of these differences, we conducted a systematic review and meta-analysis of approved drugs in these models. We searched on PubMed and Embase on July 3, 2019 for studies including either model, a monotherapy arm with an EMA/FDA approved drug for the treatment of type 2 diabetes, HbA1c assessment and a control group. Studies aimed at diabetes prevention or with surgical interventions were excluded. We calculated the Standardised Mean Difference (SMD) to compare effect sizes (HbA1c reduction) per drug and drug class across models. We included a risk of bias assessment for all included publications. A total of 121 publications met our inclusion criteria. For drugs with more than two comparisons, both models predicted the direction of the effect regarding HbA1c levels. There were no differences between the db/db mouse and ZDF rat, except for exenatide (P = 0.02) and GLP-1 agonists (P = 0.03) in which a larger effect size was calculated in the ZDF rat. Our results indicate the differences between the db/db mouse and ZDF rat are not relevant for preliminary efficacy testing. This methodology can be used to further differentiate between animal models used for the same indication, facilitating the selection of models more likely to predict human response.

Correction: A standardised framework to identify optimal animal models for efficacy assessment in drug development.

[This corrects the article DOI: 10.1371/journal.pone.0218014.].

Are some animal models more equal than others? A case study on the translational value of animal models of efficacy for Alzheimer's disease.

Clinical trial failures (>99%) in Alzheimer's disease are in stark contrast to positive efficacy data in animals. We evaluated the correlation between animal and clinical efficacy outcomes (cognition) in Alzheimer's disease using data from registered drugs as well as interventions tested in phase II or III clinical trials for Alzheimer's disease. We identified 20 interventions, which were tested in 208 animal studies in 63 different animal models. Clinical outcome was correlated with animal results in 58% of cases. But, individual animal models showed divergent results across interventions, individual interventions showed divergent results across animal models, and animal model outcomes were determined with 16 different methods. This result is unsurprising due to poor external validity (what do we model) of the animal models. Although the animal models all share Alzheimer's disease symptoms, none represents the whole syndrome. Investigators did not motivate why one model was chosen over another, and did not consider the ways the disease phenomena were generated (spontaneous, (experimentally) induced or by genetic modification), or the species characteristics, which determine the outcomes. The explanation for the lack of correlation between animal and human outcomes can be manifold: the pathogenesis of Alzheimer's disease is not reflected in the animal model or the outcomes are not comparable. Our conclusion is that currently no animal models exist which are predictive for the efficacy of interventions for Alzheimer's disease.

A standardised framework to identify optimal animal models for efficacy assessment in drug development.

INTRODUCTION: Poor translation of efficacy data derived from animal models can lead to clinical trials unlikely to benefit patients-or even put them at risk-and is a potential contributor to costly and unnecessary attrition in drug development. OBJECTIVES: To develop a tool to assess, validate and compare the clinical translatability of animal models used for the preliminary assessment of efficacy. DESIGN AND RESULTS: We performed a scoping review to identify the key aspects used to validate animal models. Eight domains (Epidemiology, Symptomatology and Natural History-SNH, Genetic, Biochemistry, Aetiology, Histology, Pharmacology and Endpoints) were identified. We drafted questions to evaluate the different facets of human disease simulation. We designed the Framework to Identify Models of Disease (FIMD) to include standardised instructions, a weighting and scoring system to compare models as well as factors to help interpret model similarity and evidence uncertainty. We also added a reporting quality and risk of bias assessment of drug intervention studies in the Pharmacological Validation domain. A web-based survey was conducted with experts from different stakeholders to gather input on the framework. We conducted a pilot study of the validation in two models for Type 2 Diabetes (T2D)-the ZDF rat and db/db mouse. Finally, we present a full validation and comparison of two animal models for Duchenne Muscular Dystrophy (DMD): the mdx mouse and GRMD dog. We show that there are significant differences between the mdx mouse and the GRMD dog, the latter mimicking the human epidemiological, SNH, and histological aspects to a greater extent than the mouse despite the overall lack of published data. CONCLUSIONS: FIMD facilitates drug development by serving as the basis to select the most relevant model that can provide meaningful data and is more likely to generate translatable results to progress drug candidates to the clinic.

Baseline imbalances and clinical outcomes of atypical antipsychotics in dementia: A meta-epidemiological study of randomized trials.

OBJECTIVES: To assess baseline imbalances in placebo-controlled trials of atypical antipsychotics in dementia, and their association with neuropsychiatric symptoms (NPS), extrapyramidal symptoms (EPS), and mortality. METHOD: We searched for trials in multiple sources. Two reviewers extracted baseline characteristics and outcomes per treatment group. We calculated direction, range, pooled mean, and heterogeneity in the baseline differences, and used meta-regression for the relationship with the outcomes. RESULTS: We identified 23 trials. Baseline type of dementia, cognitive impairment and NPS were poorly reported. The drug group had a higher mean age than the placebo group in nine trials and lower mean age in three trials (p = 0.073). The difference in percentage men between the drug and placebo group ranged from -9.7% to 4.4%. There were no statistically significant pooled baseline differences, but heterogeneity was present for age. Higher mean age at baseline in the drug versus placebo group was significantly associated with greater reduction in NPS, and higher percentage of non-White persons with lower risk of EPS. Imbalances were not significantly associated with risk of mortality. CONCLUSION: Randomized trials of atypical antipsychotics in dementia showed baseline imbalances that were associated with higher efficacy and lower risk of EPS for atypical antipsychotics versus placebo.

The safety, efficacy and regulatory triangle in drug development: Impact for animal models and the use of animals.

Nonclinical studies in animals are conducted to demonstrate proof-of-concept, mechanism of action and safety of new drugs. For a large part, in particular safety assessment, studies are done in compliance with international regulatory guidance. However, animal models supporting the initiation of clinical trials have their limitations, related to uncertainty regarding the predictive value for a clinical condition. The 3Rs principles (refinement, reduction and replacement) are better applied nowadays, with a more comprehensive application with respect to the original definition. This regards also regulatory guidance, so that opportunities exist to revise or reduce regulatory guidance with the perspective that the optimal balance between scientifically relevant data and animal wellbeing or a reduction in animal use can be achieved. In this manuscript we review the connections in the triangle between nonclinical efficacy/safety studies and regulatory aspects, with focus on in vivo testing of drugs. These connections differ for different drugs (chemistry-based low molecular weight compounds, recombinant proteins, cell therapy or gene therapy products). Regarding animal models and their translational value we focus on regulatory aspects and indications where scientific outcomes warrant changes, reduction or replacement, like for, e.g., biosimilar evaluation and safety testing of monoclonal antibodies. On the other hand, we present applications where translational value has been clearly demonstrated, e.g., immunosuppressives in transplantation. Especially for drugs of more recent date like recombinant proteins, cell therapy products and gene therapy products, a regulatory approach that allows the possibility to conduct combined efficacy/safety testing in validated animal models should strengthen scientific outcomes and improve translational value, while reducing the numbers of animals necessary.

Contribution of animal studies to evaluate the similarity of biosimilars to reference products.

The European Union (EU) was the first region to establish a regulatory framework for biosimilars, in which animal studies are required to confirm similarity to a reference product. However, animal studies described in European public assessment reports (EPARs) or marketing authorization applications (MAAs) did not identify clinically or toxicologically relevant differences despite differences in quality, suggesting that animal studies lack the sensitivity to confirm biosimilarity. Scientific advice provided learning opportunities to evolve existing guidance. Altogether, the data support a step-wise approach to develop biosimilars that focuses on quality and clinical efficacy of biosimilar. This approach might be more effective and does not necessarily require animal studies, which is also reflected in new EU draft guidance.

Immunogenicity of mAbs in non-human primates during nonclinical safety assessment.

The immunogenicity of biopharmaceuticals used in clinical practice remains an unsolved challenge in drug development. Non-human primates (NHPs) are often the only relevant animal model for the development of monoclonal antibodies (mAbs), but the immune response of NHPs to therapeutic mAbs is not considered to be predictive of the response in humans because of species differences. In this study, we accessed the drug registration files of all mAbs registered in the European Union to establish the relative immunogenicity of mAbs in NHPs and humans. The incidence of formation of antidrug-antibodies in NHPs and patients was comparable in only 59% of the cases. In addition, the type of antidrug-antibody response was different in NHP and humans in 59% of the cases. Humanization did not necessarily reduce immunogenicity in humans. Immunogenicity interfered with the safety assessment during non-clinical drug development when clearing or neutralizing antibodies were formed. While important to interpret the study results, immunogenicity reduced the quality of NHP data in safety assessment. These findings confirm that the ability to compare relative immunogenicity of mAbs in NHPs and humans is low. Furthermore, immunogenicity limits the value of informative NHP studies.

Measures of biosimilarity in monoclonal antibodies in oncology: the case of bevacizumab.

Biosimilars have been available on the European market since 2006 and experience with their use is increasing. The next wave of biopharmaceuticals that are about to lose patent protection consists of more-complicated products, including many monoclonal antibodies. Guidance has been released on the particulars of a biosimilarity exercise involving these products. Considerable challenges exist to establish biosimilarity for anticancer products. An especially challenging product is bevacizumab (Avastin(®)). On the basis of data available for the innovator product (bevacizumab) we will discuss strengths and weaknesses of preclinical and clinical models and explore the application of novel endpoints to the biosimilar comparability exercise.

The ability of animal studies to detect serious post marketing adverse events is limited.

The value of animal studies to assess drug safety is unclear because many such studies are biased and have methodological shortcomings. We studied whether post-marketing serious adverse reactions to small molecule drugs could have been detected on the basis of animal study data included in drug registration files. Of 93 serious adverse reactions related to 43 small molecule drugs, only 19% were identified in animal studies as a true positive outcome, which suggests that data from animal studies are of limited value to pharmacovigilance activities. Our study shows that drug registration files can be used to study the predictive value of animal studies and that the value of animal studies in all stages of the drug development should be investigated in a collaborative endeavour between regulatory authorities, industry, and academia.

Thirty years of preclinical safety evaluation of biopharmaceuticals: Did scientific progress lead to appropriate regulatory guidance?

INTRODUCTION: The first biopharmaceuticals were developed 30 years ago. Biopharmaceuticals differ significantly from small molecule therapeutics (SMTs). Because of such differences, it was expected that classical preclinical safety evaluation procedures applied to SMTs would not predict the adverse effects of biopharmaceuticals. Therefore, until sufficient experience was gained, the preclinical safety evaluation of biopharmaceuticals was carried out on a case-by-case basis. 30 years of experience has since expanded the knowledge base in this area, in the hope to design a preclinical safety evaluation procedure suited to biopharmaceuticals. AREAS COVERED: This review describes how the preclinical safety evaluation of biopharmaceuticals has evolved. It shows that, as result of the risk-averse behavior of regulators and industry, classical procedures were taken as starting point although state-of-the-art knowledge on biopharmaceuticals was directed towards creating a new procedure, driven by the specific properties of biopharmaceuticals. EXPERT OPINION: Current preclinical safety evaluation guidance of biopharmaceuticals is criticized because it employs a checkbox approach. The adverse effects induced by biopharmaceuticals are on-target or immune system-induced, therefore, the preclinical safety evaluation should not be standardized, but rather driven by product specific safety concerns.