UID:
almahu_9949602160102882
Umfang:
1 online resource (424 pages)
Ausgabe:
1st ed.
ISBN:
9783030336561
Serie:
Handbook of Experimental Pharmacology Series ; v.257
Anmerkung:
Intro -- Preface -- Contents -- Quality in Non-GxP Research Environment -- 1 Why Do We Need a Quality Standard in Research? -- 2 Critical Points to Consider Before Implementing a Quality Standard in Research -- 2.1 GxP or Non-GxP Standard Implementation in Research? -- 2.1.1 Diverse Quality Mind-Set -- 2.2 Resource Constraints -- 3 Non-GxP Research Standard Basics -- 3.1 Data Integrity Principles: ALCOA+ -- 3.2 Research Quality System Core Elements -- 3.2.1 Management and Governance -- 3.2.2 Secure Research Documentation and Data Management -- 3.2.3 Method and Assay Qualification -- 3.2.4 Material, Reagents and Samples Management -- 3.2.5 Facility, Equipment and Computerized System Management -- 3.2.6 Personnel and Training Records Management -- 3.2.7 Outsourcing/External Collaborations -- 3.3 Risk- and Principle-Based Quality System Assessment Approach -- 4 How Can the Community Move Forward? -- 4.1 Promoting Quality Culture -- 4.1.1 Raising Scientist Awareness, Training and Mentoring -- 4.1.2 Empowering of Associates -- 4.1.3 Incentives for Behaviours Which Support Research Quality -- 4.1.4 Promoting a Positive Error Culture -- 4.2 Creating a Recognized Quality Standard in Research: IMI Initiative - EQIPD -- 4.3 Funders Plan to Enhance Reproducibility and Transparency -- 5 Conclusion -- References -- Guidelines and Initiatives for Good Research Practice -- 1 Introduction -- 2 Guidelines and Resources Aimed at Improving Reproducibility and Robustness in Preclinical Data -- 2.1 Funders/Granting Agencies/Policy Makers -- 2.2 Publishers/Journal Groups -- 2.3 Summary of Overarching Themes -- 3 Gaps and Looking to the Future -- References -- Learning from Principles of Evidence-Based Medicine to Optimize Nonclinical Research Practices -- 1 Introduction.
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2 Current Context of Nonclinical, Nonregulated Experimental Pharmacology Study Conduct: Purposes and Processes Across Sectors -- 2.1 Outcomes and Deliverables of Nonclinical Pharmacology Studies in Industry and Academia -- 2.2 Scientific Integrity: Responsible Conduct of Research and Awareness of Cognitive Bias -- 2.3 Initiating a Research Project and Documenting Prior Evidence -- 2.4 Existence and Use of Guidelines -- 2.5 Use of Experimental Bias Reduction Measures in Study Design and Execution -- 2.6 Biostatistics: Access and Use to Enable Appropriate Design of Nonclinical Pharmacology Studies -- 2.7 Data Integrity, Reporting, and Sharing -- 3 Overcoming Obstacles and Further Learning from Principles of Evidence-Based Medicine -- 3.1 Working Together to Improve Nonclinical Data Reliability -- 3.2 Enhancing Capabilities, from Training to Open Access to Data -- 4 Conclusion and Perspectives -- References -- General Principles of Preclinical Study Design -- 1 An Overview -- 2 General Scientific Methods for Designing In Vivo Experiments -- 2.1 Hypotheses and Effect Size -- 2.2 Groups, Experimental Unit and Sample Size -- 2.3 Measurements and Outcome Measures -- 2.4 Independent Variables and Analysis -- 3 Experimental Biases: Definitions and Methods to Reduce Them -- 4 Experimental Biases: Major Domains and General Principles -- 5 Existing Guidelines and How to Use Them -- 6 Exploratory and Confirmatory Research -- References -- Resolving the Tension Between Exploration and Confirmation in Preclinical Biomedical Research -- 1 Introduction -- 2 Discrimination Between Exploration and Confirmation -- 3 Exploration Must Lead to a High Rate of False Positives -- 4 The Garden of Forking Paths -- 5 Confirmation Must Weed Out the False Positives of Exploration -- 6 Exact Replication Does Not Equal Confirmation.
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7 Design, Analysis, and Interpretation of Exploratory vs Confirmatory Studies -- 8 No Publication Without Confirmation? -- 9 Team Science and Preclinical Multicenter Trials -- 10 Resolving the Tension Between Exploration and Confirmation -- References -- Blinding and Randomization -- 1 Randomization and Blinding: Need for Disambiguation -- 2 Randomization -- 2.1 Varieties of Randomization -- 2.1.1 Simple Randomization -- 2.1.2 Block Randomization -- 2.1.3 Stratified Randomization -- 2.1.4 The Case of Within-Subject Study Designs -- 2.2 Tools to Conduct Randomization -- 2.3 Randomization: Exceptions and Special Cases -- 3 Blinding -- 3.1 Fit-for-Purpose Blinding -- 3.1.1 Assumed Blinding -- 3.1.2 Partial Blinding -- 3.1.3 Full Blinding -- 3.2 Implementation of Blinding -- 4 Concluding Recommendations -- References -- Out of Control? Managing Baseline Variability in Experimental Studies with Control Groups -- 1 What Are Control Groups? -- 2 Basic Considerations for Control Groups -- 2.1 Attribution of Animals to Control Groups -- 2.2 What Group Size for Control Groups? -- 2.3 Controls and Blinding -- 3 Primary Controls -- 3.1 Choosing Appropriate Control Treatments: Not All Negative Controls Are Equal -- 3.2 Vehicle Controls -- 3.3 Sham Controls -- 3.4 Non-neutral Control Groups -- 3.5 Controls for Mutant, Transgenic and Knockout Animals -- 4 Positive Controls -- 5 Secondary Controls -- 5.1 Can Baseline Values Be Used as Control? -- 5.2 Historical Control Values -- 6 When Are Control Groups Not Necessary? -- 7 Conclusion -- References -- Quality of Research Tools -- 1 Introduction -- 2 Drugs in the Twenty-First Century -- 2.1 Chemical Tools Versus Drugs -- 3 First Things First: Identity and Purity -- 3.1 The Case of Evans Blue -- 3.2 Identity and Purity of Research Reagents -- 4 Drug Specificity or Drug Selectivity? -- 5 Species Selectivity.
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5.1 Animal Strain and Preclinical Efficacy Using In Vivo Models -- 5.2 Differences in Sequence of Biological Target -- 5.3 Metabolism -- 6 What We Dose Is Not Always Directly Responsible for the Effects We See -- 6.1 Conditions Where In Vitro Potency Measures Do Not Align -- 7 Chemical Modalities: Not All Drugs Are Created Equal -- 8 Receptor Occupancy and Target Engagement -- 9 Radioligands and PET Ligands as Chemical Tools -- 10 Monoclonal Antibodies as Target Validation Tools -- 10.1 Targets Amenable to Validation by mAbs -- 10.2 The Four Pillars for In Vivo Studies -- 10.3 Quality Control of Antibody Preparation -- 10.4 Isotype -- 10.5 Selectivity -- 11 Parting Thoughts -- References -- Genetic Background and Sex: Impact on Generalizability of Research Findings in Pharmacology Studies -- 1 Introduction -- 2 Genetic Background: The Importance of Strain and Substrain -- 3 Importance of Including Sex as a Variable -- 4 Pharmacokinetic and Pharmacodynamic Differences Attributable to Sex -- 5 Improving Reproducibility Through Heterogeneity -- 6 Good Research Practices in Pharmacology Include Considerations for Sex, Strain, and Age: Advantages and Limitations -- 7 Conclusions and Recommendations -- References -- Building Robustness into Translational Research -- 1 Introduction -- 2 Homogeneous vs. Heterogeneous Models -- 2.1 Animal Species and Strain -- 2.2 Sex of Animals -- 2.3 Age -- 2.4 Comorbidities -- 3 Translational Bias -- 3.1 Single Versus Multiple Pathophysiologies -- 3.2 Timing of Intervention -- 3.3 Pharmacokinetics and Dosage Choice -- 4 Conclusions -- References -- Minimum Information and Quality Standards for Conducting, Reporting, and Organizing In Vitro Research -- 1 Introduction: Why Details Matter -- 2 Efforts to Standardize In Vitro Protocols -- 2.1 The MIAME Guidelines -- 2.2 The MIBBI Portal -- 2.3 Protocol Repositories.
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3 The Role of Ontologies for In Vitro Studies -- 3.1 Ontologies for Cells and Cell Lines -- 3.2 The BioAssay Ontology -- 3.3 Applications of the BAO to Bioassay Databases -- 4 Specific Examples: Quality Requirements for In Vitro Research -- 4.1 Chemical Probes -- 4.2 Cell Line Authentication -- 4.3 Antibody Validation -- 4.4 Webtools Without Minimal Information Criteria -- 4.5 General Guidelines for Reporting In Vitro Research -- 5 Open Questions and Remaining Issues -- 5.1 Guidelines vs. Standards -- 5.2 Compliance and Acceptance -- 5.3 Coordinated Efforts -- 5.4 Format and Structured Data -- 6 Concluding Remarks -- References -- Minimum Information in In Vivo Research -- 1 Introduction -- 2 General Aspects -- 3 Behavioural Experiments -- 4 Anaesthesia and Analgesia -- 5 Ex Vivo Biochemical and Histological Analysis -- 6 Histology -- 7 Ex Vivo Biochemical Analysis -- 8 Perspective -- References -- A Reckless Guide to P-values -- 1 Introduction -- 1.1 On the Role of Statistics -- 2 All About P-values -- 2.1 Hypothesis Test and Significance Test -- 2.2 Contradictory Instructions -- 2.3 Evidence Is Local -- Error Rates Are Global -- 2.4 On the Scaling of P-values -- 2.5 Power and Expected P-values -- 3 Practical Problems with P-values -- 3.1 The Significance Filter Exaggeration Machine -- 3.2 Multiple Comparisons -- 3.3 P-hacking -- 3.4 What Is a Statistical Model? -- 4 P-values and Inference -- References -- Electronic Lab Notebooks and Experimental Design Assistants -- 1 Paper vs. Electronic Lab Notebooks -- 2 Finding an eLN -- 3 Levels of Quality for eLNs -- 4 Assistance with Experimental Design -- 5 Data-Related Quality Aspects of eLNs -- 6 The LN as the Central Element of Data Management -- 7 Organizing and Documenting Experiments -- References -- Data Storage -- 1 Introduction -- 2 Data Storage Systems -- 2.1 Types of Storage.
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2.2 Features of Storage Systems.
Weitere Ausg.:
Print version: Bespalov, Anton Good Research Practice in Non-Clinical Pharmacology and Biomedicine Cham : Springer International Publishing AG,c2020 ISBN 9783030336554
Sprache:
Englisch
Schlagwort(e):
Electronic books.
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