Translational Medicine in CNS Drug Development

George G. Nomikos, Douglas E. Feltner

• Publisher: Academic Press
• Year: 2019
• Print ISBN: 9780128031612
• eBook ISBN: 9780128031643
• Topic: Biowissenschaften
• Subtopic: Pharmakologie

Translational Medicine in CNS Drug Development, Volume 29, is the first book of its kind to offer a comprehensive overview of the latest developments in translational medicine and biomarker techniques. With extensive coverage on all aspects of biomarkers and personalized medicine, and numerous chapters devoted to the best strategies for developing drugs that target specific disorders, this book presents an essential reference for researchers in neuroscience and pharmacology who need the most up-to-date techniques for the successful development of drugs to treat central nervous system disorders.Despite increases in the number of individuals suffering from CNS-related disorders, the development and approval of drugs for their treatment have been hampered by inefficiencies in advancing compounds from preclinical discovery to the clinic. However, in the past decades, game-changing strides have been made in our understanding of the pathophysiology of CNS disorders and the relationship of drug exposure in plasma and CNS to pharmacodynamic measures in both animals and humans.- Includes comprehensive coverage of biomarker tools and the role of personalized medicine in CNS drug development- Discusses strategies for drug development for a full range of CNS indications, with particular attention to neuropsychiatric and neurocognitive disorders- Includes chapters written by international experts from industry and academia

Chapter 1

Translating Animal Models of Obesity and Diabetes to the Clinic

B.M. Geiger*; E.N. Pothos^{† *} Department of Biomedical and Nutritional Sciences, Zuckerberg College of Health Sciences, University of Massachusetts, Lowell, Lowell, MA, United States
^† Program in Pharmacology and Experimental Therapeutics and Pharmacology and Drug Development, Sackler School of Graduate Biomedical Sciences and Department of Immunology, Tufts University School of Medicine, Boston, MA, United States

Abstract

Animal models of disease and drug action have played a vital role in the translation of the effects of CNS drugs from bench to bedside. Because of the limitations in our ability to understand the molecular mechanisms underlying the pathophysiology of disease in the human brain, we rely on animal models to provide insight into these mechanisms and identify promising targets for the development of therapeutics for various diseases. In this chapter, we discuss the key attributes of a valid animal model. We also elaborate on several examples of animal models used in preclinical research, including spontaneous, selectively bred, genetic, surgical, pharmacological, behavioral, or environmental models. Finally, we describe how these models have been used for the discovery and development of obesity therapeutics.

Keywords

Preclinical models; Translational research; Obesity; Animal models; Drug discovery

I Introduction

Preclinical models of disease, specifically animal models, are a necessary tool for a robust and innovative drug development process. The use of animal models to identify and validate pharmacological targets has been part of drug development for decades. Experiments in cell culture systems can provide valuable molecular mechanistic information, but they cannot typically model the more complex interactions of multiple cell types that often contribute to the pathophysiology of a disease. Animal models that contribute to our understanding of the CNS are especially important, as acquiring tissue to study from the human brain is not generally feasible or permissible. In this chapter, we discuss the various attributes of valid animal models and illustrate specific examples of animals that we use for translational research in obesity and diabetes.

II Attributes of Valid Animal Models

Animal models can be extremely valuable in the translation of our understanding of various diseases to treatments for those diseases in the clinic. Good disease models have some, if not all, of the following attributes: (1) pathophysiological similarities to human disease, (2) a phenotypic match to the disease state, (3) replicability and/or reproducibility, and (4) cost-efficiency (Blass, 2015). The first two criteria directly address the validity of the model (i.e., whether and how it relates to the disease it is supposed to model).

A Pathophysiological Similarities to Human Systems

When considering which animal models to use, one of the first criteria to consider should be how closely does the onset and development of the disease in the animal mimic the corresponding human disease. For example, in humans, an increase in an individual's body weight and excess blood glucose over time will contribute to decreased sensitivity of their insulin receptors and the eventual development of type 2 diabetes (Reinehr, 2013; Weiss et al., 2004). Therefore, a good model of this disease would also develop insulin resistance over time as body weight, adiposity, and blood glucose levels increase. As an example, C57Bl/6 mouse models of diet-induced obesity have been shown to follow the same metabolic progression of the disease as seen in humans and develop hyperglycemia and insulin resistance over time, similarly to the human disease (Collins, Martin, Surwit, & Robidoux, 2004; Petro et al., 2004; Speakman, Hambly, Mitchell, & Krol, 2007; Wang & Liao, 2012). In studying these models, researchers have been able to elucidate molecular mechanisms involved in the development of insulin resistance over time. We discuss the utility of diet-induced obesity models in greater detail later in this chapter.

B Phenotypic Match to Disease State

Other times, models may be used, where the exact pathophysiology of a disease is not a match to the human pathophysiology, but the presentation of the disease in the model is similar to the human condition. These models are referred to as phenotypic models of disease. The phenotype of a model is the set of characteristics, usually disease-related, that can be observed. If a model is a phenotypic match to a disease, then it has similar observable traits as a human who has that disease. For example, the ob/ob mouse exhibits extreme weight gain in the form of excess adipose tissue, similarly to human obesity. However, the development of obesity in the ob/ob mouse arises from leptin deficiency, while the vast majority of obese individuals have high levels of leptin. Only a very small number of human obese patients are actually leptin-deficient (Farooqi, 2008; Farooqi et al., 2007; Farooqi & O'Rahilly, 2008; Wang, Chandrasekera, & Pippin, 2014). Therefore, this model matches the phenotype of obesity, while the pathophysiology presents differences. Investigators should use caution when using models that are phenotypic models of disease to elucidate the molecular mechanisms of the disease. The altered underlying physiology may not match the human pathophysiology of the disease.

C Replicability and Reproducibility

Replicability of a model is the ability to use the same model within a research group and achieve similar results. When conducting research using animal models, a study will often span several months of data collection and use animals that have been born to different dams or in different litters. In this case, replicability of the model is extremely important. If the model is developed using an environmental insult, like 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) injections for the generation of a Parkinson's disease rodent model, consistent use of the same stereotaxic coordinates is necessary to ensure that the insult is in the same portion of the brain. The results will also replicate better if the same lot and batch of MPTP is used throughout the duration of the study. Numerous other details of the experiment must also be controlled in order to produce results that are replicable (Jackson-Lewis & Przedborski, 2007; Meredith & Rademacher, 2011). In the case of transgenic animal models, the best design to ensure replicability is to use the wild-type littermates of the knockout animals, supplemented by the appropriate backcrossing to ensure genes neighboring to the targeted ones are not affected, as opposed to a more general wild-type animal like the C57Bl6 mouse (Masca et al., 2015; Wolfer, Crusio, & Lipp, 2002).

Reproducibility is the ability of other researchers to reproduce the model in their lab and achieve similar results to those of the original research. Investigators can most easily reproduce results when the original description of the development and use of the model is clear (Masca et al., 2015). Using the same examples as in the preceding text, if a group is reproducing the results from an MPTP-induced Parkinson's disease model, but does not have the coordinates or dosage of MPTP used in the original model, reproduction of the original results will be extremely difficult, as different parameters could easily result in ablation of a different set of neurons than the original research. Additionally, many times a study will limit the model to a specific gender. If the reproducing group uses the opposite gender, they may find that gender differences complicate their ability to reproduce the original results. Furthermore, with all animal models, reproducibility is difficult when different litters of animals are used. This is particularly problematic if the animals are from different suppliers as this introduces more variability in the animals’ genetic background and microbiome, which could influence the study results (Masca et al., 2015). Sometimes, animals of the same species, gender, and phenotype may manifest important differences in behavior, physiology, disease state, or microbiome even if they come from different batches of the same or different suppliers housed in different locations and fed different diets (Ussar et al., 2015).

D Cost Efficiency

Finally, the cost of husbandry of the various animal models varies widely and should be ...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Chapter 1: Translating Animal Models of Obesity and Diabetes to the Clinic
• Chapter 2: Biomarker-Guided Drug Development for Better Defined Early Patient Studies and Clinical Trial Efficiency
• Chapter 3: Modeling and Simulation in the Translational Pharmacology of CNS Drugs
• Chapter 4: Functional Measurements of Central Nervous System Drug Effects in Early Human Drug Development
• Chapter 5: Experimental Medicine Approaches in CNS Drug Development
• Chapter 6: New Approaches in Translational Medicine for Phase I Clinical Trials of CNS Drugs
• Chapter 7: Translational Approaches for Antidepressant Drug Development
• Chapter 8: Biomarker Opportunities to Enrich Clinical Trial Populations for Drug Development in Schizophrenia and Depression
• Chapter 9: Applications of Neuroimaging Biomarkers in CNS Drug Development
• Chapter 10: PET Occupancy and Competition in Translational Medicine and CNS Drug Development
• Chapter 11: Stable Isotope Labeling Kinetics in CNS Translational Medicine: Introduction to SILK Technology
• Chapter 12: Applications of Neurophysiological Biomarkers in CNS Drug Development: Focus on Psychoses
• Chapter 13: Heart Rate Variability as a Translational Biomarker for Emotional and Cognitive Deficits
• Chapter 14: Drug Discovery in Psychiatry: Time for Human Genome-Guided Solutions
• Chapter 15: Use of Cognition to Guide Decisions About the Safety and Efficacy of Drugs in Early-Phase Clinical Trials
• Chapter 16: Digital Biomarkers in Clinical Drug Development
• Chapter 17: Lessons Learned From Public Private Partnerships and Consortia: The ADNI Paradigm
• Chapter 18: Regulatory Perspectives on the Use of Biomarkers and Personalized Medicine in CNS Drug Development: The FDA Viewpoint
• Chapter 19: Regulatory Considerations for the Use of Biomarkers and Personalized Medicine in CNS Drug Development: A European Perspective
• Chapter 20: Regulatory Science Objectives and Biomarker Qualification Through Public-Private Partnerships Are Critical to Delivering Innovative Treatments for CNS Diseases
• Chapter 21: The Assessment of Cognition in Translational Medicine: A Contrast Between the Approaches Used in Alzheimer's Disease and Major Depressive Disorder
• Chapter 22: Translational Medicine Strategies in Drug Development for Neurodevelopmental Disorders
• Chapter 23: Translational Medicine Strategies in Drug Development for Mood Disorders
• Chapter 24: Translational Medicine Strategies in Alzheimer's Disease Drug Development
• Chapter 25: Experimental Medicine Models in Generalized Anxiety Disorder and Social Anxiety Disorder
• Chapter 26: Translational Medicine Strategies in PTSD Drug Development
• Chapter 27: Unmet Medical Needs in the Treatment of Depression and the Clinical Development of a Differentiated Antidepressant: A Translational Line of Evidence
• Chapter 28: Translating Neurobiology into Practice in Tobacco, Alcohol, Drug, and Behavioral Addictions
• Chapter 29: Translational Medicine Strategies for Drug Development for Impulsive Aggression
• Chapter 30: Hypothesizing Major Depression as a Subset of Reward Deficiency Syndrome (RDS) Linked to Polymorphic Reward Genes: Considerations for Translational Medicine Approaches for Future Drug Development
• Chapter 31: Traveling Through the Storm: Leveraging Virtual Patient Monitoring and Artificial Intelligence to Observe, Predict, and Affect Patient Behavior in CNS Drug Development
• Index