One-Sided Test in A Superiority Trial

In this year’s ATS meeting, the results for a study “Zemaira in Subjects With Emphysema Due to Alpha1-Proteinase Inhibitor (API) Deficiency” (study acronym RAPID trial) was presented. The study was designed to test the superiority of Zemaira to Placebo in CT density endpoints. “According to study findings, the annual rate of lung density loss was significantly less in A1PI-treated patients (-1.45 +/- 0.24 units vs. -2.19 +/-0.25 units; p = 0.017, one sided).”  The interesting thing is that the one-sided test was used for testing differences between two treatment groups and one-sided  p-value was presented.   

The appropriateness of two- or one-sided tests has been the subject of controversy for over half a century. However, in clinical trials for regulatory approval, two-sided test is preferred and is almost uniformly adopted by the industry. ICH Guidance E9 “ STATISTICAL PRINCIPLES FOR CLINICAL TRIALS” clearly stated the followings:

“It is important to clarify whether one- or two-sided tests of statistical significance will be used, and in particular to justify prospectively the use of one-sided tests. If hypothesis tests are not considered appropriate, then the alternative process for arriving at statistical conclusions should be given. The issue of one-sided or two-sided approaches to inference is controversial and a diversity of views can be found in the statistical literature. The approach of setting type I errors for one-sided tests at half the conventional type I error used in two-sided tests is preferable in regulatory settings. This promotes consistency with the two-sided confidence intervals that are generally appropriate for estimating the possible size of the difference between two treatments.”

In RAPID trial mentioned above, the one-sided p-value was calculated as 0.017. This p-value would need to be compared with half of the conventionally significance level of 0.05. While p value of 0.017 is still statistically significant comparing to 0.025, people may wonder while two-sided p-value was not calculated for comparison to 0.05. In superiority trial, a p-value from one-sided test needs to be compared with 0.025 and a p-value from two-sided test needs to be compared with 0.05. Presenting the one-sided p-value may give a false impression that the study result is more significant since only smaller p-value is presented and the smaller (half) significance level is not presented. If the significant level is clearly stated and is presented along with the p-value, there will be no difference in understanding and interpretation of the results no matter whether one-sided or two-sided p-values are presented. If the one-sided p-value is 0.029, the result will not be statistically significant since the one-sided p-value needs to be compared to 0.025 instead of 0.05 even though it may give an wrong impression of a statistical significance.

Typically, clinical trials are designed as using two-sided test for primary efficacy endpoint. Randomized, controlled clinical trials are conducted due to the uncertainty of experimental treatment better than the comparator – equipoise. The statistical test must also consider the possibility (or probability) of experimental treatment is better than comparator or comparator is better than experimental treatment. This justifies the use of two-sided test.

If the benefit of experimental treatment is known to be better than the comparator - lack of equipoise, the one-sided test could be used, but the clinical trial would not be ethical to be conducted due to the lack of equipoise.

Drug for Treating Rare Diseases: Orphan Drug, Orphan Disease, Orphan Subset

Section 526(a) of the Federal Food, Drug and Cosmetic Act (FD&C Act) defines a ‘‘rare disease or condition’’ as following:

any disease or condition which (A) affects less than 200,000 persons in the United States, or (B) affects more than 200,000 in the United States and for which there is no reasonable expectation that the cost of developing and making available in the United States a drug for such disease or condition will be recovered from sales in the United States of such drug. Determinations under the preceding sentence with respect to any drug shall be made on the basis of the facts and circumstances as of the date the request for designation of the drug under this subsection is made.

In “Ophan drug act final rule” issued on June 12, 2013, Orphan Drug Regulations further clarified the term “ophan subject”

‘‘orphan subset’’ of persons with a particular disease or condition that otherwise affects 200,000 or more persons in the United States (‘‘non-rare disease or condition’’), for the purpose of designating a drug for use in that subset.

Regulations provided the incentives for sponsors to develop the drugs for orphan diseases. The incentives includes:

• Seven-year marketing exclusivity to the first sponsor obtaining FDA approval of a designated drug
• Tax credit equal to 50% of clinical investigation expenses
• Exemption/Waiver of PDUFA application (filing) fees
• Assistance in the drug development process
• Orphan Products Grant funding

However, in order to obtain the approval, the clinical trials are still needed to demonstrate the efficacy and safety. The requirements specified in FDA guidance “Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products” will still be met.

A debatable question is whether or not there should be less requirement for orphan drug development studies:

• Should one single pivotal study be sufficient for approval?
• Should the surrogate endpoint or biomarkers be used?
• Should there be different requirement for the size of so called ‘safety database’?
• Should alternative clinical trial design or different statistical approaches be used? 
• Should there be different drug approval pathways for orphan diseases? 

Given that the size of the patient population may vary in great deal depending on the type of orphan disease (orphan versus ultra orphan), it is not possible for the regulators to have an one set of rules that could apply to all orphan disease. The general desire from the sponsor side or patient advocate groups is to have less requirements for drug development in orphan disease.

The National Organization for Rare Disorders (NORD), is a unique federation of voluntary health organizations dedicated to helping people with rare "orphan" diseases and assisting the organizations that serve them. NORD is committed to the identification, treatment, and cure of rare disorders through programs of education, advocacy, research, and service.

Everylife Foundation for Rare Diseases is an organization dedicated to accelerating biotech innovation for rare disease treatments through science-driven public policy. They have organized a series of rare disease workshops to facilitate the process and help guide improvements in the development process for rare disease treatments. The most recent workshop is on “Accelerated Approval for Rare Disease Treatments”. All presentation slides are posted for free. 

Additional references:

• FDA/CDER Small Business Chronicles - Orphan Drugs

• FDA: Designating an Orphan Product: Drugs and Biologics
• FDA Basics Videos: Time Cote on Orphan Drugs
• Hughes et al “Drugs for exceptionally rare diseases: do they deserve special status for funding?”
• Regulatory Framework for Drugs for Rare Diseases
• Orphanet Journal of Rare Diseases

The Size of Safety Database In Drug Development Program

In planning the clinical program for drug approval, in addition to the efficacy assessment, the adequate size of the safety database is usually required by the regulatory agencies. Unlike the efficacy assessment while the sample size can be calculated, the size of safety database is a little bit vague. The size of safety database is often included in the pre-IND  meeting with FDA because the sponsor would like to understand the expectation from FDA regarding the size of safety database. There could be situations that the sample size for demonstrating the efficacy is relatively small and the overall clinical development (cost) would largely depend on the size of safety database.

The reference guidelines regarding the size of safety database are mainly the ICH E1 (The Extent of Population Exposure to Assess Clinical Safety for Drugs Intended for Long-term Treatment of Non-Life-Threatening-Conditions) and FDA guidance “Premarketing Risk Assessment”. The guidance is pretty specific in terms of the size of safety database “For products intended for long-term treatment of non-life-threatening conditions, (e.g., continuous treatment for 6 months or more or recurrent intermittent treatment where cumulative treatment equals or exceeds 6 months)

the ICH and FDA have generally recommended that 1500 subjects be exposed to the investigational product (with 300 to 600 exposed for 6 months, and 100 exposed for 1 year).  For those products characterized as chronic use products in the ICH guidance E1A, FDA recommends that the 1500 subjects include only those who have been exposed to the product in multiple dose studies, because many adverse events of concern (e.g., hepatotoxicity, hematologic events) do not appear with single doses or very short-term exposure. Also, the 300 to 600 subjects exposed for 6 months and 100 subjects exposed for 1 year should have been exposed to relevant doses (i.e., doses generally in the therapeutic range)

 

In product label, the corresponding sample database description is provided in FDA guidance “Adverse Reactions Section of Labeling for Human Prescription Drug and Biological Products — Content and Format”

The data described below reflect exposure to drug X in [n] patients, including [n] exposed for 6 months and [n] exposed forgreater than one year. Drug X was studied primarily in placeboand active-controlled trials (n = __, and n = ___, respectively), and in long-term follow up studies. The population was [age range], [gender distribution], [race distribution] and had [diseases/conditions]. Most patients received doses [describerange, route of administration, frequency, duration, as appropriate].

 

However, for products intended for short-term or acute use and for products intended to treat life-threatening diseases, the guidance did not provide the specific requirement. The requirement of the size of safety database is usually based on the discussion with the review devision of the regulaory agency. FDA guidance “Premarketing Risk Assessment” did provide some general guidelines:

“Safety databases for products intended to treat life-threatening diseases, especially in circumstances where there are no alternative satisfactory treatments, are usually smaller than for products intended to treat diseases that are neither life-threatening nor associated with major, irreversible morbidity. A larger safety database may be appropriate if a product’s preclinical assessment or human clinical pharmacology studies identify signals of risk that warrant additional clinical data to properly define the risk. The appropriate size of the preapproval safety database may warrant specific discussion with the relevant review division. For instance, 21 CFR 312.82(b) (subpart E) provides that for drugs intended to treat life-threatening and seriously debilitating illnesses, end-of-phase 1 meetings can be used to agree on the design of phase 2 trials “with the goal that such testing will be adequate to provide sufficient data on the drug’s safety and effectiveness to support a decision on its approvability for marketing.”

 “For products intended for short-term or acute use ( e.g., treatments that continue for, or are cumulatively administered for, less than 6 months), FDA believes it is difficult to offer general guidance on the appropriate target size of clinical safety databases. This is because of the wide range of indications and diseases (e.g., acute strokes to mild headaches) that may be targeted by such therapies. Sponsors are therefore encouraged to discuss with the relevant review division the appropriate size of the safety database for such products. Because products intended for lifethreatening and severely debilitating diseases are often approved with relatively small safety databases, relatively greater uncertainty remains regarding their adverse effects. Similarly, when products offer a unique, clinically important benefit to a population or patient group, less certainty in characterizing risk prior to approval may be acceptable.”

 

It is no surprise if FDA provides the following guidance to the sponsor “FDA has generally accepted a minimum of 300-400 subjects as the safety database to support an indication in the intended use population(s) to rule out with 95% confidence an adverse event that occurs with a frequency of 1%. “

Apparently, the above statement requires statistical calculation (or beyond the statistical calculation). If we use the SAS Proc Freq, we can actually see how the number 300-400 is played out.

data samplesize;

  input scenario Event $ count;

  datalines;

  1 AE 1

  1 no 294

  2 AE 1

  2 no 299

  3 AE 1

  3 no 554

  4 AE 1

  4 no 559

run;

proc freq data=samplesize;

  weight count;

  tables Event /  binomial (p=0.01) alpha=0.05 cl; 

       **p=0.01 option indicates the standard rate to compare with,

         here we assume the AE rate of 1%;

       ** Alpha=0.05 to obtain two side 95% confidence interval;

  exact binomial;  *Obtain the exact p-value;

  by scenario;

run;

The scenarios 1 and 2 are based on the calculation of the asymptotic CI:

For N=295 and event=1, the 95% CI =(0.0000, 0.0100), which meant with 295 subject safety database, it would not be able to rule out an AE that occurs with a frequency of 1%;

For N=300, and event=1, the 95%CI=(0.0000, 0.0099), which meant with 300 subject safety database, it would be able to rule out an AE that occurs with a frequency of 1%

The scenarios 3 and 4 are based on the calculation of the exact CI:

For N=555 and event=1, the 95% CI =(0.0000, 0.0100), which meant with 555 subject safety database, it would not be able to rule out an AE that occurs with a frequency of 1%;

For N=560, and event=1, the 95%CI=(0.0000, 0.0099), which meant with 560 subject safety database, it would be able to rule out an AE that occurs with a frequency of 1%

Even though the exact confidence interval is more appropriate when dealing with such low frequency of AE. It may be inpractical to impose a safety database of 560 subjects treated with the testing product.

The size of safety database may depend on the specific area in development. For example, FDA has its requirement for safety database on vaccine product. Below is the specific requirement from FDA guidance “Guidance for Industry: Clinical Data Needed to Support the Licensure of Seasonal Inactivated Influenza Vaccines”

“Safety data must be collected from subjects enrolled in pre-licensure clinical trials intended to support the accelerated approval of a new seasonal inactivated influenza vaccine (21 CFR 312.23, 312.32, 312.56, 312.60 and 312.62). The monitoring of these subjects should follow the outline for safety evaluations described in Section III.A.3. above. A total safety database large enough to rule out a serious adverse event that occurs at a rate of 1 in 300 may be sufficient when a sponsor has adequate marketing and safety experience with the same manufacturing process for a seasonal vaccine licensed outside the United States and these data are presented in the BLA and assessed as such. For example, the upper limit of the two-sided 95% CI of the true serious adverse event rate is 0.0032 ( less than 1 in 300) when no serious adverse event is observed among 1150 subjects who received vaccine in clinical trials, using the Clopper-Pearson method. However, the size of the pre-licensure safety database, especially for seasonal influenza vaccines manufactured using novel processes such as cell-culture and for seasonal influenza vaccines that contain novel adjuvants, would be influenced by factors such as the nature of the new manufacturing process and available pre-clinical and clinical data, and should be discussed with CBER.

Moreover, if a serious adverse event is present in a safety database of about 1,000 subjects, and there is concern that it may be vaccine-related, additional safety data may be needed. Safety data to support use in pediatric populations would also be needed and should be submitted either as part of the BLA, or as a clinical efficacy supplement at a later time, if pediatric studies are deferred under PREA (see Section III.C.4. - Pediatric Research Equity Act).”

 

Safety Database versus Safety Population

These two terms can sometimes cause confusion. Safety Database includes only the subjects who exposed to the testing drug and not control. Safety database is usually used for discussion of the entire clinical program (multiple studies). Safety Population is the term used in clinical protocol or statistical analysis plan pertinent to a specific protocol. Safety population defined as the subjects who received any dose of the study medication (includes both testing drug and control).

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?