Uncertainty in Pharmacology: Epistemology, Methods, and Decisions (Boston Studies in the Philosophy and History of Science, 338)

Preface
Contents
List of Contributors
Part I Epistemology
	1 Defining Aspects of Mechanisms: Evidence-Based Mechanism (Evidence for a Mechanism), Mechanism-Based Evidence (Evidence from a Mechanism), and Mechanistic Reasoning
		1.1 Introduction
		1.2 Defining “Mechanism”
			1.2.1 Etymology
			1.2.2 Usage
			1.2.3 Modern Intensional Definitions of “Mechanism”
			1.2.4 Some Examples
			1.2.5 Definitions of “Mechanism” in Terms of Different Forms of Operation
		1.3 “Evidence of Mechanism”: The Problem of “of”
		1.4 Evidence-Based Mechanism (Evidence for a Mechanism) and Mechanism-Based Evidence (Evidence from a Mechanism)
			1.4.1 Evidence-Based Mechanism (Evidence for a Mechanism)
				1.4.1.1 Ontic Evidence
				1.4.1.2 Anagnoristic Evidence
			1.4.2 Mechanism-Based Evidence (Evidence from a Mechanism)
				1.4.2.1 Analysing How Adverse Drug Reactions Can Be Caused and Prevented (Aronson and Ferner 2010)
				1.4.2.2 Explanation of Outcomes
				1.4.2.3 Extrapolation
				1.4.2.4 Praxis
				1.4.2.5 Drug Discovery
				1.4.2.6 Hypothesis Generation
		1.5 Mechanistic Reasoning
		1.6 Mechanisms Without Trials and Trials Without Mechanisms
			1.6.1 Mechanisms Without Trials
			1.6.2 Trials Without Mechanisms
		1.7 Conclusions
		References
	2 Causal Insights from Failure: Post-marketing Risk Assessment of Drugs as a Way to Uncover Causal Mechanisms
		2.1 Deep Causal Knowledge for Pharmacology
		2.2 What Is This Thing Called Causal Science?
		2.3 The Nature of Causation
		2.4 The Dispositional Nature of Mechanisms
		2.5 The Knowledge Potential of Post-marketing Monitoring
		2.6 How Much Failure Do We Need?
		2.7 Reporting Suspected Causal Failure: A Community Effort
		2.8 Conclusion
		References
	3 Extrapolating from Model Organisms in Pharmacology
		3.1 Introduction
		3.2 Model Organisms in Pharmacology
		3.3 Case Studies
		3.4 Strategies for Extrapolation
		3.5 The Logic of Extrapolation
		3.6 Conclusion
		References
	4 Mechanistic vs Statistical Extrapolation in Preclinical Research in Psychiatry: Challenging the Received View
		4.1 Introduction
		4.2 The Received View on Evidence Based Upon Preclinical Studies
			4.2.1 Inferences from Preclinical Studies Consist in Comparative Tracing
			4.2.2 Evidence of a Mechanism Is the Main Point of Preclinical Studies
		4.3 Challenging the Received View on Preclinical Evidence
			4.3.1 The Effects of Selective Serotonin Reuptake Inhibitors Have Not Been Established by Mechanistic Extrapolation: A Case Study
			4.3.2 The Effects of Selective Serotonin Reuptake Inhibitors Were Established by Statistical Extrapolation
		4.4 Objections to the Existence of Statistical Extrapolation
			4.4.1 Standardization Is Not Evidence
			4.4.2 Statistical Extrapolation Is Rare
			4.4.3 Statistical Extrapolation Must Rely on Mechanistic Thinking
			4.4.4 Statistical Extrapolation Is Applied Research in a Specific Case But Eventually Relies on Basic, Mechanistic Research on General Processes
		4.5 Conclusion
		References
	5 Analogy-Based Inference Patterns in Pharmacological Research
		5.1 Introduction: Scientific Inference in Pharmacology
			5.1.1 Evidence Amalgamation and Hypothesis Confirmation
			5.1.2 Analogy as Inferential Pattern
		5.2 Learning from Relevant Evidence
			5.2.1 Heterogeneous Evidence
			5.2.2 Relevance
			5.2.3 Measuring Distance
				5.2.3.1 Similarity of Numeric Properties
				5.2.3.2 Similarity of Structural Properties
			5.2.4 Extrapolating with Good Arguments and Breaking the Extrapolator\'s Circle
				5.2.4.1 What Can Make RCT Evidence Relevant?
				5.2.4.2 The Extrapolator\'s Circle
		5.3 Transferring Knowledge from Confirmed Causal Links
		5.4 Confirmatory Support from In Silico Simulation
		5.5 Conclusions
		References
	6 In Silico Clinical Trials: A Possible Response to Complexity in Pharmacology
		6.1 Introduction: The Complexity of Pharmacology
		6.2 RCTs: The State of the Art
		6.3 In Silico Trials for Disease Modeling
		6.4 ISCTs: What Kind of Issues Do They Address?
		6.5 The Promise of in silico Strategies: The Avicenna Project and Its Relevance to Current Pharmacological Challenges
		6.6 How Could in silico Approaches Extend and Integrate Traditional Clinical Trials
		6.7 Conclusion
		References
	7 Uncertainty in Drug Discovery: Strategies, Heuristics and Technologies
		7.1 Introduction
		7.2 The Changing Face of the Drug Development Pipeline
		7.3 High-Throughput Screening
			7.3.1 Introduction
			7.3.2 HTS Methods
			7.3.3 HTS and the Leaky Pipeline
		7.4 Druglikeness, the Increasingly Leaky Pipeline
		7.5 Lipinski\'s Rule of Five
		7.6 Conclusions
		References
	8 ``Caught in the Amber\'\': A Sketch of ChemicalUnderdetermination
		8.1 Introduction
		8.2 Epistemological Underdetermination
		8.3 Philosophy of Chemistry in Response to EU: An Example
		8.4 Ontological Underdetermination
		8.5 A Notorious Pharmacological Example
		8.6 Conclusion
		References
Part II Methods
	9 A Millian Look at the Logic of Clinical Trials
		9.1 Introduction
		9.2 Clinical Trials
		9.3 Assessing Hypotheses with the Method of Difference
			9.3.1 Hypotheses of Sufficient Causality
			9.3.2 Hypotheses of Statistical Causality
			9.3.3 Hypotheses of Efficacy
		9.4 Mill\'s Method and the Cognitive Structure of Scientific Commonsense
		9.5 Concluding Remarks
		References
	10 Learning by Difference: Placebo Effects and Specific Efficacy in Pharmacological RCTs
		10.1 Introduction
		10.2 Why Are RCTs Used to Test Pharmacological Interventions?
		10.3 The Logic Beneath RCTs and the Assumption of Specific Efficacy (ASE)
		10.4 Falsifying the ASE: Some Epistemic Consequences
		10.5 Placebo Responses as Plausible Confounders in Pharmacological RCTs
			10.5.1 Placebo Non-specific Effects May Interact with Drug Specific Effects
			10.5.2 Unequal Placebo Non-specific Effects Resulting from Unsuccessful Blinding
		10.6 Epistemic Strategies for Saving the Assumption of Specific Efficacy
			10.6.1 Different Trial Designs to Assess the Contribution of Specific Drug Effects and Participants\' Expectations
			10.6.2 Active Placebos and Retrospective Tests for Securing and Testing Blinding Success
		10.7 Conclusion
		References
	11 An Evidence-Hierarchical Decision Aid for Ranking in Evidence-Based Medicine
		11.1 Introduction
		11.2 The Decision Problem
			11.2.1 Related Methodological Work
				11.2.1.1 Evidence Hierarchies
				11.2.1.2 GRADE
				11.2.1.3 Medical Decision Support Literature for Evidence Amalgamation: A broader Perspective
			11.2.2 Multi Criteria Decision Making for this Ranking Problem
				11.2.2.1 Landscapes of Multi Criteria Decision Making
				11.2.2.2 Purely Ordinal MCDM
			11.2.3 Methodology of this Approach
		11.3 HiDAD
			11.3.1 Evidence Appreciation
			11.3.2 Aggregation of Comparisons
			11.3.3 The Ranking
		11.4 Properties of HiDAD
			11.4.1 Compatibility
			11.4.2 Further Properties
		11.5 Modifications of HiDAD
		11.6 Applicability of HiDAD
			11.6.1 Potential Further Areas of Application
			11.6.2 Restricted Scope for Application
		11.7 Conclusions
			11.7.1 Claims
				11.7.1.1 Normative Claims Made
				11.7.1.2 Claims Not Made
			11.7.2 Assessing Properties of HiDAD
		References
	12 Assessing Drug Safety Assessment: Metformin Associated Lactic Acidosis
		12.1 Introduction
		12.2 Assessing Medicines
			12.2.1 Assessing Efficacy
			12.2.2 Assessing Safety
		12.3 Causal Assessment
		12.4 Metformin Associated Lactic Acidosis
			12.4.1 Evidence of Difference-Making
			12.4.2 Evidence of Mechanisms
				12.4.2.1 Do Therapeutic Doses of Metformin Increase Blood Lactate Levels?
				12.4.2.2 Are There Case Reports of Metformin Associated Lactic Acidosis in Which Metformin is the Only Likely Contributor?
				12.4.2.3 Is the Severity of Lactic Acidosis Related to Metformin Blood Concentrations?
		12.5 Causal Assessment Is Superior to a Method-Focused Approach to Assessing Drug Safety
		12.6 Conclusion
		References
	13 Robust Biomarkers: Methodologically Tracking Causal Processes in Alzheimer\'s Measurement
		13.1 Introduction
		13.2 Robustness Analysis
			13.2.1 Robustness Analysis: Uses
			13.2.2 Individuating Modes
		13.3 Surrogate Markers
			13.3.1 Surrogate Markers and Clinical Standards
			13.3.2 Biomarkers in Practice: Problems with Uncertainty and Divergence.
		13.4 Robustness Analysis and Divergence
			13.4.1 Individuating Modes; (Partially) Independent Pathways vs. Physical Principles
			13.4.2 Robustness and Elimination: Specifying Causal Relations.
		13.5 Robustness Analysis Applied to Alzheimer\'s Research
			13.5.1 Alzheimer\'s and Theoretical Revision
			13.5.2 Intervention-Based Robustness Analysis Applied to Alzheimer\'s Measurement
		13.6 Concluding Remarks
		References
	14 Modelling Individual Response to Treatment and Its Uncertainty: A Review of Statistical Methods and Challenges for Future Research
		14.1 Introduction
		14.2 Overview of Standard Statistical Approaches
			14.2.1 Settings
			14.2.2 Subgroup Analyses
			14.2.3 Regression Modelling with Treatment-Covariate Interactions
			14.2.4 Individual Treatment Effects and Potential Outcome
			14.2.5 Statistical Challenges: Some Examples
		14.3 Challenges: Reporting, Statistics, and Ethics
			14.3.1 Good Reporting Challenges
			14.3.2 Simple Risk Calculations
			14.3.3 Ethical Aspects in Connection with Statistics
		14.4 Discussion
		14.5 Epistemological Summary
		A.1 Statistical Excursus: Cox Regression and Logistic Regression
		References
	15 Epistemic Gains and Epistemic Games: Reliability and Higher Order Evidence in Medicine and Pharmacology
		15.1 Introduction
		15.2 Isolating Causes vs. Causes in Interaction: The Two Contending Paradigms
		15.3 The Elitist and the Pluralist Game
			15.3.1 The Elitist Game
				15.3.1.1 Random Error
				15.3.1.2 Systematic Error
			15.3.2 The Pluralist Game
			15.3.3 Context-Sensitivity of Causality and Causal Modulation
		15.4 E-Synthesis: A Probabilistic Causal Inference with Heterogeneous and Higher Order Evidence
		15.5 Discussion
			15.5.1 The EBM vs. Pluralist Approach to Causal Inference
			15.5.2 Evidence Hierarchies
			15.5.3 Causal Holism
			15.5.4 Evidence of Mechanisms and Relevance
			15.5.5 Reliability and Higher Order Evidence
		References
Part III Decisions
	16 Values in Pharmacology
		16.1 Introduction
		16.2 Some Concepts for Value Characterization
			16.2.1 Intrinsic and Extrinsic Values
			16.2.2 Moral Values
			16.2.3 Epistemic and Non-Epistemic Values
		16.3 Ethical Values in Pharmacology
			16.3.1 Weighing Risks and Benefits
			16.3.2 Enhancements
			16.3.3 An Asymmetrical Weighing?
			16.3.4 Procedural Values and the Double Requirement
			16.3.5 Other-Regarding and Social Values
		16.4 Pharmacology and the Science-Value Issue
			16.4.1 Two Types of Decisions
			16.4.2 A Baseline that We Should Not Go Below
			16.4.3 Raising the Demands for Practical Reasons
			16.4.4 Dealing with Requirements to Go Below the Baseline
			16.4.5 A Two-Branched Strategy
		16.5 Conclusion
		References
	17 Humbug, the Council of Pharmacy and Chemistry, and the Origin of “The Blind Test” of Therapeutic Efficacy
		17.1 A Practical Scientific System
		17.2 Fleecing the Sick and Miserable
		17.3 The Turbidity of Fermentation
		17.4 The Crucial Proof of the Pudding Is in the Eating Therof
		17.5 A Plea and a Program for Work
		Historian\'s Appendix A: Non Sibi Sed Medicinae
		Historian\'s Appendix B: A Brief History of Randomization, Blinding, and Control
		References
	18 On the Normative Foundations of Pharmaceutical Regulation
		18.1 Why Pharmaceutical Regulation?
		18.2 Liberal Arguments for Regulation
		18.3 Regulatory Paternalism and Risk Aversion
		18.4 RCTS and the Justification of Regulatory Paternalism
		18.5 Regulatory Paternalism and Impartiality
		18.6 A Socialist View of Pharmaceutical Regulation
		18.7 A Libertarian View of Pharmaceutical Regulation
		18.8 Concluding Remarks
		References
	19 After Disclosure
		19.1 Introduction
		19.2 The Nature and Scope of Industry Funding Bias
		19.3 The Magnitude of Industry Bias
		19.4 Proposed Remedies
			19.4.1 Disclosure
			19.4.2 Further Standards and Regulations for Research
			19.4.3 Independent Sponsorship of Clinical Research
			19.4.4 Case by Case Assessments
		19.5 Proposed Interventions
		19.6 Conclusions
		References
	20 Sex, Drugs, and How to Deal with Criticism: The Caseof Flibanserin
		20.1 Introduction
		20.2 Establishing the Clinical Evidence on Flibanserin
			20.2.1 The Approval of Flibanserin
			20.2.2 Methodological and Conceptual Issues of the Flibanserin Studies
		20.3 Assessing the Diagnosis
		20.4 Risk-Benefit Assessment and the Role of Advocacy
		20.5 Conclusion
		References