Biomarkers versus Surrogate Endpoints

“Biomarkers” and “Surrogate endpoints” are closely related, although a biomarker can serve as a surrogate endpoint, the terms of biomarkers and surrogate endpoints are not synonymous.

FDA Guidance for Industry “Qualification Process for Drug Development Tools” provided the clear definitions for biomarker and surrogate endppoint.

A biological marker or biomarker is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or biological responses to a therapeutic intervention.⁴ A biomarker can define a physiologic, pathologic, or anatomic characteristic or measurement that is thought to relate to some aspect of normal or abnormal biologic function. Changes in biomarkers following treatment may predict or identify safety problems related to a drug candidate or reveal a pharmacological activity expected to predict an eventual benefit from treatment. Biomarkers may reduce uncertainty in drug development and evaluation by providing quantitative predictions about drug performance.

A surrogate endpoint is defined as a biomarker intended to substitute for a clinical efficacy  endpoint. Surrogate endpoints are expected to predict clinical benefit (or harm, or lack of benefit or harm). A clinical endpoint is defined as a characteristic or variable that reflects how a patient feels, functions, or survives.

 

In the subsequent FDA public workshop “ Measurement in Clinical Trials: Review and Qualification of Clinical Outcome Assessments”. a glossary included the slight different definition for biomarker and surrogate endpoint.

Biomarker — A patient characteristic that is measured as an indicator of biologic processes; normal, pathogenic, or response to a therapeutic intervention. Patient characteristics that are classified as biomarkers are those that are not significantly influenced by rater judgment or patient motivation and effort (e.g., not a measure of a patient’s volitional performance of some defined procedure). Measurements that can (and therefore, should) have a sufficiently well defined procedure for measurement so that minor aspects of rater or technician involvement have no impact on the measurement are included within the category of biomarkers. Biomarkers are not psychomodulated measures.

Surrogate endpoint — An indirect outcome measure that is used as a substitute for a direct measurement of how a patient feels or functions. All biomarkers used as a clinical trial outcome assessment are surrogate endpoints or proposed as surrogate endpoints. The acceptability of a surrogate endpoint is dependent upon a demonstration that it can be used to reliably infer treatment benefit. The term is also sometimes applied to indirect psychomodulated measures to emphasize that they are indirect, and a substitute (replacement) for a direct measure of how a patient feels or functions.

 

In a paper by Strimbu and Tavel “What are biomarkers?”, biomarker and surrogate endpoint were defined as following:

Biomarkers are by definition objective, quantifiable characteristics of biological processes. They may but do not necessarily correlate with a patient's experience and sense of wellbeing, and it is easy to imagine measurable biological characteristics that do not correspond to patients' clinical state, or whose variations are undetectable and without effect on health. It is also even easier to imagine measurable biological characteristics whose variance among populations is so great as to render them all but useless as reliable predictors of disease or its absence.

In contrast, clinical endpoints are variables that reflect or characterize how a subject in a study or clinical trial “feels, functions, or survives”. They are, in other words, variables that represent a study subject's health and wellbeing from the subject's perspective.

When used as outcomes in clinical trials, biomarkers are considered to be surrogate endpoints; that is, they act as surrogates or substitutes for clinically meaningful endpoints. But, not all biomarkers are surrogate endpoints, nor are they all intended to be. Surrogate endpoints are a small subset of well-characterized biomarkers with well-evaluated clinical relevance. To be considered a surrogate endpoint, there must be solid scientific evidence (e.g., epidemiological, therapeutic, and/or pathophysiological) that a biomarker consistently and accurately predicts a clinical outcome, either a benefit or harm. In this sense, a surrogate endpoint is a biomarker that can be trusted to serve as a stand-in for, but not as a replacement of, a clinical endpoint.

FDA Guidance for Industry “Qualification Process for Drug Development Tools” provided the detail descriptions for the biomarkers.

Biomarkers include measurements that suggest the etiology of, susceptibility to, activity levels of, or progress of a disease. Alterations in biomarker measurements indicate responses  (favorable or unfavorable) related to an intervention. The biomarker may reflect biological processes closely related to the mechanism of action or processes substantially downstream. Biomarkers may assess many different types of biological characteristics or parameters, including

• genetic composition (e.g., BRCA, HER2)   
• receptor expression patterns
• radiographic or other imaging-based measurements (e.g., progressive free survival (PFS), CT lung densitometry, thrombolysis through arteriogram,...)
• blood composition measurements (e.g., serum enzyme levels, prostate specific antigen) electrocardiographic parameters
• organ function (e.g., creatinine clearance, pulmonary function tests, cardiac ejection fraction)

Biomarkers can be categorized into three categories:

Prognostic biomarker	a baseline patient or disease characteristic that categorizes patients by degree of risk for disease occurrence or progression. A prognostic biomarker informs about the natural history of the disorder in that particular patient in the absence of a therapeutic intervention.
Predictive biomarker	a baseline characteristic that categorizes patients by their likelihood for response to a particular treatment. A predictive biomarker is used to identify whether a given patient is likely to respond to a treatment intervention in a particular way. It may predict a favorable response or an unfavorable response (i.e., adverse event).
pharmacodynamic (or activity) biomarker	a dynamic assessment that shows that a biological response has occurred in a patient after having received a therapeutic intervention. A pharmacodynamic biomarker may be treatment-specific or more broadly informative of disease response. Examples include blood pressure, cholesterol, HbA1C, intraocular pressure, radiographic measures, and C-reactive protein.

• Surrogate endpoints are a subset of pharmacodynamic biomarkers; it is likely that only a few biomarkers will be appropriate for use as surrogate endpoints. In other words, only pharmacodynamic biomarker may be qualified as surrogate endpoint.  
• Prognostic biomarker can be very useful in sub-group analyses.
• Predictive biomarker can be very useful in designing the enrichment designs.

In Summary,          

• Surrogate endpoints are biomarkers
• Not all biomarkers can serve as surrogate endpoints
• Surrogate endpoints are a subset of pharmacodynamic biomarkers;
• Surrogate endpoint should predict/be correlated with clinical endpoint, but correlation alone is insufficient

Age Calculation in Clinical Trial Data Analysis with SAS Examples

In clinical trials, subject’s age is a critical demographic variable that needs to be collected. Age may be used in checking the inclusion/exclusion criteria, in sub-group analysis, in prognostic factor analysis, and so on. However, the Age variable is not directly collected on the case report form. Instead, the birth date is collected. Subject’s age will then need to be calculated based on the birth date and the date of screening visit. There are many ways in calculating the age and the results are slightly different. There seems to be no consensus in industry or CDISC indicating which method should be used. There was a paper discussing “How to Create Variables Related to Age”, some of the new functions were not included in the discussion.

Since we use SAS to analyze the clinical trial data, I listed the various ways to calculate the Age using SAS.

/*Macro AGE1 uses SAS INTCK function*/

%macro age1(from=,to=);

   intck ('year', &from, &to) -

       ((month(&to) < month(&from)) or

       ((month(&to) = month(&from)) and (day(&to) < day(&from))));

%mend;

/*Macro AGE2 uses SAS INTCK function*/

%macro age2(from=,to=); 

      floor ((intck('month',&from,&to) - (day(&to) < day(&from))) / 12)

%mend age2;

data try;

  input bdate date9. idate date9.;

  age1=%age1(from=bdate,to=idate); 

  age2=%age2(from=bdate, to=idate);

  age3=yrdif(bdate,idate, 'Age');       *This method uses SAS YRDIF function;

  age365=(idate-bdate)/365;           *This method uses 365 as divider;

  age36525=(idate-bdate)/365.25;  *This method uses 365.25 as divider;

  age36525plus1=Floor(((idate-bdate)+1)/365.25);   *This method adds 1 and the divide by 365.25;

datalines;

01JAN12  31DEC12    /*this is one day short of one year*/

01JAN12  31DEC12    /*this is exactly one year*/

31DEC12  01JAN13    /*this is exactly one day, but cross the year*/

01JAN04  01JAN13    /*this is exactly nine years*/

01JUL12  01JAN13    /*this is 184 days*/

;

run;

proc print;

format bdate date9. idate date9.;

run;

bdate	idate	age1	age2	age3	age365	age36525	age36525 plus1
01JAN2012	31DEC2012	0	0	1.00000	1.00000	0.99932	1
01JAN2012	01JAN2013	1	1	1.00000	1.00274	1.00205	1
31DEC2012	01JAN2013	0	0	0.00278	0.00274	0.00274	0
01JAN2004	01JAN2013	9	9	9.00000	9.00822	9.00205	9
01JUL2012	01JAN2013	0	0	0.50000	0.50411	0.50376	0

Based on the outputs, the calculation using YRDIF seems to be a good option. The use of YRDIF function is detailed in the SAS online document. The simple way using 365.25 as divider is actually a pretty good option. There is also an article in SAS blog discussing this. For an adult study, all of these approaches in calculating Age seem to be ok. However, for pediatric studies, different approaches could give quite bit different Age calculations.  

On the regulatory side, there seems to be various ways in calculating the Age based on the documents submitted to FDA. In BLA 125145, Age in month was calculated as (1st vaccination date - Date of birth + 1) / (365.25/12). In SBA for EUFLEXXA approval, Age was calculated as: age = (date of informed consent - date of birth) / 365.25. In clinical review document for Actemra, the duration in study (in years) was calculated as:  

Duration in study (years) = (date of last assessment – date of first TCZ dose +1) 365.25).

Hy’s Law and Drug-Induced Liver Injury (DILI)

For clinical laboratory data analyses, statistical tabulations are typically generated to list the number of subjects in each treatment group with ALT, AST, TBL with n times of ULN (upper limit of normal (range)). For AST and ALT, n=3 and for TBL, n=2.

ALT, AST, TBL are all “liver enzymes” and are liver function test parameters. Other liver test parameters may also include GGTP and ALP, and others.

• ALT (alanine aminotransferase or SGPT)
• AST( aspartate transaminase or  SGOT), 
• TBL (total bilirubin)
• GGTP (gamma-glutamyl transpeptidase)
• ALP (alkaline phosphatase)

In clincal trials, liver test parameters are the basis for assessing the so-called DILI (drug-induced liver injury).

Acording to FDA’s guidance “ Drug-Induced Liver Injury: Premarketing Clinical Evaluation“, when assessing the DILI, Hy’s law can be followed. Hy’s law is based on the work by Hy Zimmerman, a major scholar of drug-induced liver injury.

Hy’s Law cases have the following three components:
1.      The drug causes hepatocellular injury, generally shown by a higher incidence of 3-fold or greater elevations above the ULN of ALT or AST than the (nonhepatotoxic) control drug or placebo
2.      Among trial subjects showing such AT elevations, often with ATs much greater than 3xULN, one or more also show elevation of serum TBL to >2xULN, without initial findings of cholestasis (elevated serum ALP)
3.      No other reason can be found to explain the combination of increased AT and TBL, such as viral hepatitis A, B, or C; preexisting or acute liver disease; or another drug capable of causing the observed injury
 Finding one Hy’s Law case in the clinical trial database is worrisome; finding two is considered highly predictive that the drug has the potential to cause severe DILI when given to a larger population.

Hy’s law and DILI assessment is also specifically mentioned in FDA CDER Review Template “Clinical Safety Review of an NDA or BLA”

At present, it appears that a potential for severe hepatotoxicity may be signaled by a set of findings sometimes called Hy’s Law, based on the observation by Hy Zimmerman, a major scholar of drug-induced liver injury (DILI), that a pure hepatocellular injury leading to jaundice had serious implications, a 10 to 50 percent mortality. Any Hy’s Law cases should be identified in the treatment group (e.g., subjects with any elevated aminotransferase (AT) of >3x upper limit of normal (ULN), alkaline phosphotase (ALP) >2xULN, and associated with an increase in bilirubin ≥2xULN).

My colleague used to argue with me about the use of 3 times x ULN for ALT and AST and cited the NCI’s CTC (common toxicity criteria) as the evidence. In NCI’s Common Toxicity Criteria, the 2.5 x ULN elevation of ALT and AST would be considered as adverse event with Grade 2 (corresponding to moderate AE severity). However, the NCI has shifted the Common Toxicity Criteria to CTCAE (Common Terminology Criteria for Adverse Events). In CTCAE, AST, ALT is consistent with the FDA guidance (i.e., 3xULN would be considered as grade 2)

Recently, FDA, industry and academia contemplate a new approach to gauging drug induced liver injury by using individual patients’ baseline (instead of ULN) liver enzyme measurements, a move that some say could eliminate problems with the use of the upper limit of normal (ULN) and allow for the assessment of DILI in cases where there is underlying liver injury.

Further Reading:

• Drug-Induced Liver Injury (DILI) – FDA CLINICAL INVESTIGATOR TRAINING COURSE
• Drug-Induced Liver Injury: Making Decisions, Making Progress by Dr throckmorton
• Issues and Controversies in DULI
• Drug Induced Liver Injury (DILI)
• Common Terminology Criteria for Adverse Events (CTCAE) Version 4.0
• Zimmerman, HJ, 1999, Drug-Induced Liver Disease
• Dr Avigan, Drug-Induced Liver Injury – Epidemilogy & Considerations of Risk
• Dr Temple, Does Pre-Existing Liver Disease Make DILI More Likely?

Randomized Start Design (RSD)

FDA recently issued its guidance "Alzheimer’s Disease: Developing Drugs for the Treatment of Early Stage Disease ". The guidance cited alternative trial designs and the Randomized Start Design (RSD) was recommended.

A randomized-start or randomized-withdrawal trial design (with clinical outcome measures) appears to be a more convincing means of demonstrating such an effect. For ethical reasons, a randomized-start design would be most appropriate for use in AD. In this study design, patients are randomized to drug and placebo, and at some point, placebo patients are crossed over to active treatment. If patients in the trial who were initially on placebo then assigned to active treatment fail to catch up (after a reasonable period of time) to patients who received active treatment for the entire duration of the trial, a disease modifying effect of treatment would have been shown. We are unaware of any instances to date where this design has been successfully used in a clinical trial to establish a disease modifying effect.

RSD has been discussed for its use in many CNS and neurological diseases such as Alzheimer’s disease, Parkinson’s disease, Multiple sclerosis, Post Polio syndrome. These diseases all have very slow progression. Treatment effect for these diseases can be separated as effect on symptomatic component and the disease modifying component.  "Disease modifying" can be defined as treatments or interventions that affect the underlying pathophysiology of the disease and have a beneficial outcome on the course of the disease.

A similar design to RSD may be called "delayed start design". The delayed-start design is used in order to overcome a problem that occurs in similar studies of medicines that may slow the progression of the disease but also have an effect on symptoms. Typically in these studies used to determine the effects of a drug on slowing disease progression, the medicines or placebo are started and stopped in all patients at the same time. After stopping the drugs, the researchers then wait for several weeks to measure the patients’ symptoms. This waiting time is referred to as the “washout” period, in which the effects of the drug are “washed out” of the body. Patients whose symptoms remain better at the end of the washout period time are presumed to have had their disease slowed by the treatment. However, no one knows what length of time is needed for a true symptomatic washout period. When a study uses a delayed-start design, all patients are presumed to be experiencing the same degree of symptom relief at the end of the trial because they are all taking the medication. If the patients who have received the medicine for the longer period of time have fewer symptoms, it is presumed to be due to the medicine slowing the effects of the disease.

Further reading:

·  The Randomized Start Design for Assessing Disease-Modifying Effects in ParkinsonDisease: Issues and Controversies by David Oakes

·  Designing clinical trials to test disease-modifying agents—application to the treatment trials of Alzheimer's disease by Xiong

·  A new proposal for randomized start design to investigate disease-modifying therapies for Alzheimer disease by Zhang

A New Drug Approval Pathway - Breakthrough Therapy Designation

Recently, a new drug approval pathway proposed by FDA has drawn attentions to many pharmaceutical/biotechnology companies. Last year, The Food and Drug Administration Safety and Innovation Act (FDASIA) included a provision that allows sponsors to request that their drug be designated as a Breakthrough Therapy. Breakthrough status means the companies will have closer communication with top FDA staff to move drugs for serious diseases to market more quickly, potentially with data from an expanded Phase 1 trial.

• US FDA announces first Breakthrough Therapy Designations
• Innovative Drugs May Win U.S. Approval After Early Trials- Novartis grabs FDA's coveted 'breakthrough' status for lung cancer drug
• Pharmacyclics Gains ‘Breakthrough’ Status for Cancer Drug
• Pfizer's Palbociclib Receives Breakthrough Therapy Designation for Breast Cancer Treatment

Only a small portion of drugs can obtain the Breakthrough Therapy designation. The drugs for treating the serious diseases with unmet medical needs may be easier to obtain the Breakthrough status. The drugs targeting the small sub-population of certain disease may also be qualified for Breakthrough Therapy designation (see the example for Vertex' Kalydeco). The clinical trial that designed to target the small sub-population is called enrichment clinical trial design (i.e., choosing sub-population that is likely to respond to the study drug, therefore design a smaller study) . See recent FDA guidance.

Only a small portion of drugs can obtain the Breakthrough Therapy designation. The drugs for treating the serious disease with unmet medical needs may be easier to obtain the Breakthrough status. The drugs targeting the small sub-population of certain disease may also be qualified for Breakthrough Therapy designation (see the example for Vertex' Kalydeco). The clinical trial that designed to target the small sub-population is called enrichment clinical trial design (i.e., choosing sub-population that is likely to respond to the study drug, therefore design a smaller study) . See

recent FDA guidance.

FDA Existing Drug Approval Pathways are:

• Accelerated approval for serious disease with unmet medical need. Approval with surrogate endpoint with commitment for confirmatory trial with clinical endpoint.
• Orphan drug status or orphan drug designation for rare diseases
• Restricted distribution Approval with restrictions to assure safe use
• Priority review FDA completes review within 6 months versus standard review time of 10 months
• Fast track review to facilitate the development, and expedite the review of drugs to treat serious diseases and fill an unmet medical need
• Breakthrough drug designation see above
• the Generating Antibiotics Incentives Now (GAIN) Act. For antibiotics

In addition, there is a movement for Patient oriented drug development (i.e., the study endpoint should measure ‘feel, function, or survival’ of the patients). See

FDA presentations on this topic.

In EU, the Adaptive Licensing is gaining more interests. The idea of Adaptive Licensing is to allow a drug to be launched onto the market with certain restrictions, based on its benefit/risk profile. Those restrictions could then be gradually removed as more positive data is forthcoming and more favorable benefit/risk profile. However, in US, there is no movement about Adaptive Licensing isea.  

Attached is FDA's Development & Approval Process (Drugs)

•  How Drugs are Developed and Approved
•  Drug and Biologic Approval Reports
•  NDA and BLA Approvals 
•  NME Drug and New Biologic Approvals
•  ANDA (Generic) Drug Approvals
•  Priority NDA and BLA Approvals
•  Efficacy Supplement Approvals
•  Accelerated and Restricted Approvals 
•  NDA Approvals by Therapeutic Potential and Chemical Type
•  Fast Track Approval Reports

COPD as a Syndrome

Typically, in medical journals, we don’t like to include a lot of formula or mathematical expressions in articles. However I recently read a paper where a mathematical expression is used to describe the COPD as a syndrome. It is a good use of mathematical expression in this case.

John J. Reilly “COPD and declining FEV1 – time to divide and conquer?” (NEJM 359:15: 1616-1618 OCT 2009)

“In fact, COPD in the singular is probably a misnomer. It is more appropriate to view COPD as a syndrome that encompasses a variety of obstructive diseases that share a common exposure but differ in terms of mechanism of disease and response to therapy. This concept is expressed in the mathematical notation

 

In which COPD_n represents subgroups of COPD. As a reflection of this recognized heterogeneity, investigator have developed new classification systems, such as the BODE index, which evaluates the body-mass index, the degree of airflow obstruction and dyspnea, and exercise capacity to create a 10-point scale in which higher scores indicate a higher risk of death. In addition, investigators have attempted to define other homogeneous subgroups of patients with COPD. “

For many complicated diseases, when we have a better understanding about the disease, we will see that one specific disease may have many different manifestations or phenotypes – so called ‘heterogeneity’. When designing a clinical trial for a disease with heterogeneity, it may be difficult to show the treatment effect on the patient population as a whole. The drug may only be effective on one of many specific sub-populations. The difficult is usually in finding this specific sub-population. The newly issued FDA guidance “Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and

Biological Products” is attempting to address this issue. 

Current Topics in Bioethics - A Free Seminar

Free Seminar on Current Topics in Bioethics By Sheila Mikhail, founder of Life Sciences Law (LSL)

The field of bioethics merges various aspects of several disciplines, including biology, medicine, politics, and law. The role of human subjects in the investigation of therapeutics gives rise to several concerns within the realm of bioethics, with such concerns increasing over time.

This presentation will provide an overview of several areas with respect to bioethics, including:

(1) ensuring the safety of study participants in human clinical trials as it relates to the process of obtaining and ensuring informed consent,

(2) the use of drugs that have not yet been approved by the FDA for patients who have no other treatment options available under compassion use principles,

(3) the ownership of biological samples isolated from patients, and

(4) the patentability of isolated gene sequences.

References:

• Tuskegee syphilis experiment
• Nazi Human Experiments
• Unethical Human Experiment in the United States
• Henrietta Lacks
• Hela Cell
• University of Washington vs Dr Catalona
• Havasupai Tribe vs. Arizona State University
• Human Specimen Collection, Biobanking and Genetic Research: PredictER Law and Policy Update
• Compassionate use of unapproved drugs
• Guatemala syphilis experiment
• Results of Death-Camp Experiments: Should They Be Used?
• Nazi Medical Experimentation: Should the Data Obtained be Used?