| 书目名称 | Nonclinical Statistics for Pharmaceutical and Biotechnology Industries | | 编辑 | Lanju Zhang | | 视频video | | | 概述 | Provides the first volume dedicated to nonclinical statistics.Brings together relevant statistical methods for nonclinical research in pharma/biotech industries with a mission promoting nonclinical st | | 丛书名称 | Statistics for Biology and Health | | 图书封面 |  | | 描述 | .This book serves as a reference text for regulatory, industry and academic statisticians and also a handy manual for entry level Statisticians. Additionally it aims to stimulate academic interest in the field of Nonclinical Statistics and promote this as an important discipline in its own right. This text brings together for the first time in a single volume a comprehensive survey of methods important to the nonclinical science areas within the pharmaceutical and biotechnology industries. Specifically the Discovery and Translational sciences, the Safety/Toxiology sciences, and the Chemistry, Manufacturing and Controls sciences. Drug discovery and development is a long and costly process. Most decisions in the drug development process are made with incomplete information. The data is rife with uncertainties and hence risky by nature. This is therefore the purview of Statistics. As such, this book aims to introduce readers to important statistical thinking and its application in thesenonclinical areas. The chapters provide as appropriate, a scientific background to the topic, relevant regulatory guidance, current statistical practice, and further research directions. . | | 出版日期 | Book 2016 | | 关键词 | Animal studies; Biomarker; Chemistry, manufacturing and controls (CMC); Drug research and development; N | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-23558-5 | | isbn_softcover | 978-3-319-79499-0 | | isbn_ebook | 978-3-319-23558-5Series ISSN 1431-8776 Series E-ISSN 2197-5671 | | issn_series | 1431-8776 | | copyright | Springer International Publishing Switzerland 2016 |
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Front Matter |
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Introduction to Nonclinical Statistics for Pharmaceutical and Biotechnology Industries |
Lanju Zhang,Cheng Su |
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Drug discovery and development is a long, complicated, risky, and costly process. Most decisions in the process are made based on data with uncertainties, which provides a natural field for statistics. Clinical statistics is a shining example of how an industry has embraced statistics as an equal partner. However, it is not the case of nonclinical statistics. In this chapter, we give a brief introduction to drug discovery and development process, including chemistry, manufacturing and controls (CMC), statistical applications in these nonclinical areas, and the current landscape of clinical and nonclinical statistics in pharmaceutical and biotechnology companies. Then we try to define nonclinical statistics, discuss the nonclinical statistical profession, identify possible causes of predicaments in nonclinical statistics, and point out potential directions to change the status quo.
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Regulatory Nonclinical Statistics |
Mohammad Atiar Rahman,Meiyu Shen,Xiaoyu (Cassie) Dong,Karl K. Lin,Yi Tsong |
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The nonclinical statistics teams in the Center of Drug Review and Research of the Food and Drug Administration (FDA) conduct regulatory reviews, statistical consultation, and statistical methodology development in nonclinical regulations. In this chapter, we provide a brief description of the two teams and provide two examples in statistical research development. In the first example, we describe the historical background and evolution of statistical methodology development in the last 20 years for the acceptance sampling and lot evaluation procedures on dose content uniformity involved with FDA Chemistry Manufacturing, and Control (CMC) Statistics Team. In the second example, we illustrate the research activities of Pharmacological/Toxicological (Pharm-Tox) Statistics Team at FDA with the background and evaluation of multiple pairwise comparisons in animal carcinogenetic studies.
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How To Be a Good Nonclinical Statistician |
Bill Pikounis Ph.D.,Luc Bijnens Ph.D. |
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All fields profess commonly expressed criteria for its individual professionals to be successful. For the pharmaceutical/biotechnology industry that is the scope of this book, there are many accounts in the field of statistics of what it takes to be a good statistician. The goal of this chapter is to focus on specific characteristics for nonclinical statisticians which we believe are essential to be viewed as “good” professionals, either as individual contributors or managers.
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Statistical Methods for Drug Discovery |
Max Kuhn,Phillip Yates,Craig Hyde |
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This chapter is a broad overview of the drug discovery process and areas where statistical input can have a key impact. The focus is primarily in a few key areas: target discovery, compound screening/optimization, and the characterization of important properties. Special attention is paid to working with assay data and phenotypic screens. A discussion of important skills for a nonclinical statistician supporting drug discovery concludes the chapter.
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High-Throughput Screening Data Analysis |
Hanspeter Gubler |
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An overview over the role and past evolution of High Throughput Screening (HTS) in early drug discovery is given and the different screening phases which are sequentially executed to progressively filter out the samples with undesired activities and properties and identify the ones of interest are outlined. The goal of a complete HTS campaign is to identify a validated set of chemical probes from a larger library of small molecules, antibodies, siRNA, etc. which lead to a desired specific modulating effect on a biological target or pathway. The main focus of this chapter is on the description and illustration of practical assay and screening data quality assurance steps and on the diverse statistical data analysis aspects which need to be considered in every screening campaign to ensure best possible data quality and best quality of extracted information in the hit selection process. The most important data processing steps in this respect are the elimination of systematic response errors (pattern detection, categorization and correction), the detailed analysis of the assay response distribution (mixture distribution modeling) in order to limit the number of false negatives and fal
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Quantitative-Structure Activity Relationship Modeling and Cheminformatics |
Max Kuhn |
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This chapter describes quantitative tools for analyzing chemical structures and relating them to assay results using statistical models. The focus is on prediction of new compounds as well as the exploratory analysis and data mining of large compound databases. Other issues related to how these analytical methods are used are discussed.
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GWAS for Drug Discovery |
Yang Lu,Katherine Perez-Morera,Rita M. Cantor |
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Genome-wide association studies (GWAS) are playing a major role in identifying genetic associations with complex traits and disorders. It is anticipated this basic work will lead to a better understanding of the etiologies of such traits and disorders. In pharmacogenetics, application of GWAS methods is evolving. Here, the traits and disorders of interest derive from the diversity in patient response to medications. It is anticipated that GWAS will be successful in identifying the genotypes of individuals who respond well to a medication with correct dosages and highlighting those who will exhibit particular side effects. The GWAS process for a medication includes: (1) an appropriate study sample with variation in the medication response; (2) genotypes measured by arrays in members of the study sample; (3) quality control, imputation and correction for population stratification on the GWAS genotypes; (4) SNP association tested with the drug response using appropriate statistical methods for categorical and/or quantitative traits and appropriately corrected levels of statistical significance. In this chapter, GWAS is motivated by a brief summary of known pharmacogenetics findings. W
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Statistical Applications in Design and Analysis of In Vitro Safety Screening Assays |
Lei Shu,Gary Gintant,Lanju Zhang |
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In this chapter, we introduce statistical applications used in the design and analysis of a high throughput in vitro screening assay, QTiSA-HT (an acronym for QT-inotropy-Screening Assay-High Throughput), a proprietary in vitro platform used to characterize concentration-dependent effects of drugs that affect cardiac repolarization and contractility. Specifically, we discuss the design and analysis of cumulative, ascending dose concentration response studies, calculation of appropriate sample sizes, and the use of statistical significance tests and equivalence margins to provide robust estimates of true drug effects based on both concurrent and historical vehicle-control data. The goal of this chapter is to showcase how we search for solutions to real scientific problems arising in early phases of drug safety screening using statistical methods and tools.
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Nonclinical Safety Assessment: An Introduction for Statisticians |
Ian S. Peers,Marie C. South |
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This chapter provides an overview of the nonclinical drug safety testing process, and the statistical challenges associated with work in this area. Whilst other chapters in this book focus on specific types of designs and analyses which you may encounter during nonclinical drug development, we provide here the context for a statistician working in nonclinical safety assessment, and seek to prepare them for some of the practical issues and decisions they are likely to face. We will describe the scope and framework for the studies run within a nonclinical safety assessment programme, and recommend when and how a statistician needs to engage to add value. We will look generally at design and analysis considerations for safety studies which, whilst not unique to this context, are particularly pertinent to this area of work. Finally we will highlight some practical considerations and industry trends. If this chapter provides you with insight that complements rather than replicates what is taught in conventional statistics courses, and inspires you to believe that statistical work in this area can be both valuable and rewarding, we will have achieved our goal.
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General Toxicology, Safety Pharmacology, Reproductive Toxicology, and Juvenile Toxicology Studies |
Steven A. Bailey,Dingzhou Li,David M. Potter |
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This chapter provides a survey of key nonclinical safety assays. For each study type, we discuss the typical study designs employed, including a summary of the type of endpoints collected. We then provide an overview of common statistical approaches in each setting. There are some general themes that are common across the study types (e.g., trend testing). At the same time, the different study types may have features that require special consideration (e.g., cross-over designs for safety pharmacology studies, intra-litter correlation in reproductive toxicology studies). While some of the design aspects of these studies are to some extent “fixed” by precedent across the industry, we do address sample size and power considerations, as this information can be valuable to understanding how statistical results can contribute to the overall interpretation of these studies. Finally, for any discussion of statistical approaches, there are likely to be multiple reasonable approaches. We’ve attempted to cover some of the more common approaches in detail, but we recognize that our treatment is not exhaustive. Where possible, we have provided references for further reading.
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Clinical Assays for Biological Macromolecules |
Jianchun Zhang,Robert J. Kubiak |
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Assessments of pharmacokinetics (PK), pharmacodynamics (PD) and immunogenicity are indispensable parts of the development process for therapeutic biologics. It is, therefore, essential to develop suitable assays for these assessments. However, development of assays for biological macromolecules poses unique challenges. In this chapter, we review the scientific background of clinical assay development and validation, as well as the common statistical methods used during the life-cycle of assay development.
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Recent Research Projects by the FDA’s Pharmacology and Toxicology Statistics Team |
Karl K. Lin,Matthew T. Jackson,Min Min,Mohammad Atiar Rahman,Steven F. Thomson |
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In addition to regular review work, the Pharmacology and Toxicology Statistics Team in CDER/FDA is actively engaged in a number of research projects. In this chapter we summarize some of our recent investigations and findings..We have conducted a simulation study (discussed in Sect. 12.2) to evaluate the increase in Type 2 error attributable to the adoption by some non-statistical scientists within the agency of more stringent decision criteria than those we have recommended for the determination of statistically significant carcinogenicity findings in long term rodent bioassays. In many cases, the probability of a Type 2 error is inflated by a factor of 1. 5 or more..A second simulation study (Sect. 12.3) has found that both the Type 1 and Type 2 error rates are highly sensitive to experimental design. In particular, designs using a dual vehicle control group are more powerful than designs using the same number of animals but a single vehicle control group, but this increase in power comes at the expense of a greatly inflated Type 1 error rate..Since the column totals of the tables of permutations of animals to treatment groups cannot be presumed to be fixed, the exact methods use
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Design and Evaluation of Drug Combination Studies |
Lanju Zhang,Hyuna Yang |
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Drug combination studies are generally conducted to look for synergistic effects. In this chapter, we discuss typical study design and analysis methods for drug combination studies, with a focus on in vitro experiments. Two reference models Loewe additivity and Bliss independence are used for synergy evaluation. A new index based on Bliss independence is introduced, comparable to the interaction index based on Loewe Additivity. An example data set is used to demonstrate the implementation of these analysis methods. In the final discussion, we point out some future research areas.
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Biomarkers |
Chris Harbron |
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Biomarkers are playing an increasingly important role throughout many aspects of the pharmaceutical discovery and development pipeline. They have many differing roles and applications and statistics plays a critical role in their discovery, validation or qualification and how they are applied and utilised. In this chapter we shall discuss what biomarkers are, the types of data that they generate and the impact on the subsequent statistical analysis, paying particular attention to the avoidance of false positives in biomarker discovery and confirming the technical performance of assays measuring biomarkers.
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Overview of Drug Development and Statistical Tools for Manufacturing and Testing |
John Peterson,Stan Altan |
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An essential part of the application for marketing approval of a new drug or therapeutic biological product submitted to regulatory authorities is the Chemistry Manufacturing and Controls (CMC) section. It presents the sponsor company’s documentation of sufficient scientific and engineering knowledge to manufacture the product with consistent quality that provides defined clinical efficacy with an acceptable safety profile. The CMC section includes three main parts: (1) Chemical Development (synthesis of a new molecular entity (NME), purification of a new biologic entity (NBE); (2) Pharmaceutical Development (comprised of formulation and process development); (3) Analytical Development (analytical methods for physical, chemical, biological characterization). Specifications are established during development and constitute an important part of the CMC section in defining the product’s quality requirements. This chapter provides an overview of the drug development process, and some statistical tools useful in support of CMC studies. This chapter aims to set the stage for the subsequent 11 chapters in the CMC section of this book which delve in greater detail into important CMC relate
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发表于 2025-3-21 18:11:23
