Guidance for Industry Exposure-Response Relationships — Study Design, Data Analysis, and Regulatory Applications

D. Measuring Response

Broadly speaking, both positive (efficacy) and negative (safety) effects of a drug can be characterized using a variety of measurements or response endpoints. These effects include clinical outcomes (clinical benefit or toxicity), effects on a well-established surrogate (change in blood pressure or QT interval), and effects on a more remote biomarker (change in ACE inhibition or bradykinin levels) thought to be pertinent to clinical effects. All of these measurements can be expected to show exposure-response relationships that can guide therapy, suggest efficacy or safety, dose and dosing intervals, or suggest a hypothesis for further study.

In many cases, multiple response endpoints are more informative than single endpoints for establishing exposure-response relationships. Specifically, less clinically persuasive endpoints (biomarkers, surrogates) can help in choosing doses for the larger and more difficult clinical endpoint trials and can suggest areas of special concern. In most cases, it is important to standardize the measurement of response endpoints across studies and between study sites and/or laboratories.

1. Biomarkers

Biological marker (biomarker) refers to a variety of physiologic, pathologic, or anatomic measurements that are thought to relate to some aspect of normal or pathological biologic processes (Temple 1995; Lesko and Atkinson 2001). These biomarkers include measurements that suggest the etiology of, the susceptibility to, or the progress of disease; measurements related to the mechanism of response to treatments; and actual clinical responses to therapeutic interventions. Biomarkers differ in their closeness to the intended therapeutic response or clinical benefit endpoints, including the following:

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  Biomarkers thought to be valid surrogates for clinical benefit (e.g., blood pressure, cholesterol, viral load)
  
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  Biomarkers thought to reflect the pathologic process and be at least candidate surrogates (e.g., brain appearance in Alzheimer’s Disease, brain infarct size, various radiographic/isotopic function tests)
  
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  Biomarkers reflecting drug action but of uncertain relation to clinical outcome (e.g., inhibition of ADP-dependent platelet aggregation, ACE inhibition)
  
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  Biomarkers that are still more remote from the clinical benefit endpoint (e.g., degree of binding to a receptor or inhibition of an agonist)
  

From a regulatory perspective, a biomarker is not considered an acceptable surrogate endpoint for a determination of efficacy of a new drug unless it has been empirically shown to function as a valid indicator of clinical benefit (i.e., is a valid surrogate). Theoretical justification alone does not meet the evidentiary standards for market access. Many biomarkers will never undergo the rigorous statistical evaluation that would establish their value as a surrogate endpoint to determine efficacy or safety, but they can still have use in drug development and regulatory decision making. Changes in biomarkers typically exhibit a time course that is different from changes in clinical endpoints and often are more directly related to the time course of plasma drug concentrations, possibly with a measurable delay. For this reason, exposure-response relationships based on biomarkers can help establish the dose range for clinical trials intended to establish efficacy. In some cases, these relationships can also indicate how soon titration should occur, and can provide insight into potential adverse effects. Biomarkers can also be useful during the drug discovery and development stage, where they can help link preclinical and early clinical exposure-response relationships and better establish dose ranges for clinical testing.

2. Surrogate Endpoint

Surrogate endpoints are a subset of biomarkers. A surrogate endpoint is a laboratory measurement or physical sign used in therapeutic trials as a substitute for a clinically meaningful endpoint that is expected to predict the effect of the therapy (Temple 1999). A well-validated surrogate endpoint will predict the clinically meaningful endpoint of an intervention (Lesko and Atkinson 2001), with consistent results in several settings. FDA is able to rely on less well-established surrogates for accelerated approval of drugs that provide meaningful benefit over existing therapies for serious or life-threatening illnesses (e.g., acquired immunodeficiency syndrome). In these cases, the surrogates are reasonably likely to predict clinical benefit based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence. However, generally, in trials examining surrogate endpoints, even where the endpoint is well correlated with a clinical outcome, surrogates will be unable to evaluate clinically relevant effects of the drug unrelated to the surrogate, whether these are beneficial or adverse (Temple 1999).

3. Clinical Benefit or Outcome Endpoints

Clinical benefit endpoints are variables that reflect how a patient feels, functions, or survives. Clinical endpoints reflect desired effects of a therapeutic intervention and are the most credible response measurements in clinical trials.

VI. MODELING OF EXPOSURE-RESPONSE RELATIONSHIPS

A. General Considerations

Safety information and adequate and well-controlled clinical studies that establish a drug’s effectiveness are the basis for approval of new drugs. Exposure-response data can be derived from these clinical studies, as well as from other preclinical and clinical studies, and provide a basis for integrated model-based analysis and simulation (Machado et al. 2000; Sheiner and Steimer 2000). Simulation is a way of predicting expected relationships between exposure and response in situations where real data are sparse or absent. There are many different types of models for the analysis of exposure-response data (e.g., descriptive PD models (Emax model for exposure-response relationships) or empirical models that link a PK model (dose-concentration relationship) and a PD model (concentration-response relationship)). Descriptive or empirical model-based analysis does not necessarily establish causality or provide a mechanistic understanding of a drug’s effect and would not ordinarily be a basis for approval of a new drug. Nevertheless, dose-response or PK-PD modeling can help in understanding the nature of exposure-response relationships and can be used to analyze adequate and well-controlled trials to extract additional insights from treatment responses. Adequate and well-controlled clinical studies that investigate several fixed doses and/or measure systemic exposure levels, when analyzed using scientifically reasonable causal models, can predict exposure-response relationships for safety and/or efficacy and provide plausible hypotheses about the effects of alternative doses and dosage regimens not actually tested. This can suggest ways to optimize dosage regimens and to individualize treatment in specific patient subsets for which there are limited data. Creating a theory or rationale to explain exposure-response relationships through modeling and simulation allows interpolation and extrapolation to better doses and responses in the general population and to subpopulations defined by certain intrinsic and extrinsic factors.