Thursday, December 26, 2013

Quantification of Risk – Breaking down adverse reaction into common and uncommon categories

When we conduct a clinical trial using an approved product, we may run into the requests for the list of common, very common adverse events. This may be especially true when the clinical trials are conducted in European countries. The requests may come from the IRB (institutional review board) or EC (ethics committee).


Breaking down the AEs into very common, common, uncommon, rare, and very rare categories is part of the requirements for SmPC (product label in EU countries). The requirements are included in various EC or EMA guidelines

Within each system organ class, the ADRs should be ranked under headings of frequency, most frequent reactions first, using the following convention:
 Very common (greater than and equal to 1/10); common (greater than and equal to 1/100 to less than 1/10); uncommon (greater than and equal to 1/1,000 to less than 1/100); rare  (greater than and equal to 1/10,000 to less than 1/1,000); very rare (less than and equal to 1/10,000), not known (cannot be estimated form the available data).

Within each frequency grouping, adverse reactions should be presented in the order of decreasing seriousness. The names used to describe each of the frequency groupings should follow standard terms established in each official language using the following convention: Very common (greater than and equal to 1/10); common (greater than and equal to 1/100 to less than 1/10); uncommon (greater than and equal to 1/1,000 to less than 1/100); rare (greater than and equal to 1/10,000 to less than 1/1,000); very rare (less than 1/10,000).
An EMA presentation specifically discussed what the Section 4.8: Undesirable effects should include in terms of quantifying the adverse drug reactions.

There are plenty of examples for showing how the adverse drug reaction tables in Summary of Product Characteristics should be presented.

In the recent issued EMA guidance for Fibrin Sealant, it also requires to breakdown the AEs into the following categories:

Tabulated list of adverse reactions

The table presented below is according to the MedDRA system organ classification (SOC and Preferred 265 Term Level).  
Frequencies have been evaluated according to the following convention: Very common (greater than and equal to 1/10); common (greater than and equal to 1/100 to less than 1/10); uncommon (greater than and equal to 1/1,000 to less than 1/100); rare (greater than and equal to 1/10,000 to less than 1/1,000); very rare (less than 1/10,000), not known (cannot be estimated from the available data).

The source for the quantification of risk for adverse reactions is the CIOMS. For example, Benefit-Risk Balance for Marketed Drugs: Evaluating Safety Signals Report of CIOMS Working Group IV has a section for “quantification of risk”

5. Quantification of Risk Incidence of the reaction
To put the newly identified risk into perspective, it is important to quantify it in terms of incidence. Precise quantification will usually be difficult in the post-marketing environment, in which most new safety signals arise from spontaneous reporting, with its associated uncertainties as to numerators (reported cases) and denominators (patient exposures). However, risk can often be approximated in terms of magnitudes of 10, as suggested in the CIOMS III report: greater than 1% (common or frequent); greater than 1 per 1000 but less than 1 per cent (uncommon or infrequent); greater than 1 per 10,000 but less than 1 per 1000 (rare); less than 1 per 10,000 (very rare). 
When possible, attempts should be made to determine whether the incidence is affected by the existence of any apparent high-risk groups. These might be defined by, for example, dose or duration of treatment, use of other drugs (e.g., drug interactions), presence of other diseases (e.g., renal failure), or special populations defined by demographics or ethnicity. In principle, one of the most important functions of risk evaluation is to identify individual patients at increased risk of serious adverse reactions. Although some mechanisms are fairly well understood (enzyme inhibition processes; drug interactions), the pharmacological and biological basis of drug-induced diseases (e.g., role of pharmacogenetics) is relatively unexplored. Work in this area is needed and should be encouraged.

In US, when we prepare the summary tables for adverse events or adverse drug reactions, we typically don’t include a table to break down the frequency into very common, common, uncommon, rare and very rare categories. The drug label did not require to include the quantification of risk using these categories. However, for drug to be approved in European countries, it is a good idea to include a summary table with these quantification categories. 

Tuesday, December 10, 2013

Pharmacokinetic studies when endogenous compounds exist and pre-dose concentrations are not zero

This Monday, FDA issued a new guidance titled “ Bioequivalence Studies with Pharmacokinetic Endpoints for Drugs Submitted Under an ANDA”. While the guidance is more for bioequivalence studies for generic drugs, a paragraph on Endogenous Compounds caught my eyes:

E. Endogenous Compounds
 Endogenous compounds are drugs that are already present in the body either because the body produces them or they are present in the normal diet. Because these compounds are identical to the drug that is being administered, determining the amount of drug released from the dosage form and absorbed by each subject can be difficult. We recommend that applicants measure and approximate the baseline endogenous levels in blood (plasma) and subtract these levels from the total concentrations measured from each subject after the drug product has been administered. In this way, you can achieve an estimate of the actual drug availability from the drug product. Depending on whether the endogenous compound is naturally produced by the body or is present in the diet, the recommended approaches for determining BE differ as follows:  When the body produces the compound, we recommend that you measure multiple baseline concentrations in the time period before administration of the study drug and subtract the baseline in an appropriate manner consistent with the pharmacokinetic properties of the drug.
  When there is dietary intake of the compound, we recommend that you strictly
control the intake both before and during the study. Subjects should be housed at a
clinic before the study and served standardized meals containing an amount of the
compound similar to that in the meals to be served on the pharmacokinetic sampling day.
 For both of the approaches above, we recommend that you determine baseline concentrations for each dosing period that are period specific. If a baseline correction results in a negative plasma concentration value, the value should be set equal to 0 before calculating the baseline-corrected AUC. Pharmacokinetic and statistical analysis should be performed on both uncorrected and corrected data. Determination of BE should be based on the baseline-corrected data.

When we study the therapeutic proteins, we often need to deal with the endogenous concentration issue. Studies using human plasma derived products (proteins) will always involve in the endogenous concentration issue since these therapeutic proteins are naturally occurring substances and are already present in the body. The pharmacology studies for these therapeutic proteins need to consider both the endogenous (already in the body) and exogenous (through augmentation) concentrations. In a book “Clinical pharmacology of therapeutic proteins” by Dr Mahmood, three approaches are discussed to deal with this issue:
  1. subtract the pre-dose concentration – baseline-corrected pharmacokinetic analysis
  2. using the sum of exogenous and endogenous proteins following the administration of exogenous protein – uncorrected pharmacokinetic analysis;
  3. the use of radio-labeled proteins to differentiate the exogenous proteins from the endogenous proteins.


For a bioequivalence study, it is easier to show the bioequivalence with approach #2 above.

When using baseline-corrected pharmacokinetic analyses, the accurate measure of the pre-dose concentration is important. If all possible, there should be multiple measures at pre-dose and then mean value of the pre-dose measuresments can be used as the baseline for correction.

In FDA’s Draft Guidance on Progesterone, it has the following comments regarding the baseline-correction.
 Please measure baseline progesterone levels at -1.0, -0.5, and 0 hours before dosing. The mean of the pre-dose progesterone levels should be used for the baseline adjustment of the post-dose levels. Baseline concentrations should be determined for each dosing period, and baseline corrections should be period specific. If a negative plasma concentration value results after baseline correction, this should be set to 0 prior to calculating the baseline-corrected AUC. Please analyze the data using both uncorrected and corrected data.
  
In a clinical pharmacology review document for a Factor XIII Concentrate, the sponsor presented the pharmacokinetic parameters based on baseline adjusted FXIII activity (Berichrom assay) and  also the pharmacokinetic parameters based on un-adjusted FXIII activity.


In summary, while both baseline-adjusted and unadjusted PK analyses are viable approaches in dealing with the existence of endogenous concentrations, the baseline-adjusted PK analyses are the safer approach to go. In this approach, the pre-dose concentration or average pre-dose concentration will be subtracted from all post-dose concentration measures before the PK parameters (for example AUC) are calculated.  

Sunday, December 08, 2013

SAS Programming for PK Time-Concentration Curve Using SGPLOT

Comparing with other software/tools for generating the graphs, SAS/Graph may be inferior. Programming to generate graphs using SAS/Graph languages can be time consuming and the quality of the graphs is usually not so great and not in publication quality. It is also not easy to output the graphs in the commonly used format. With SAS/Graph, we usually create the graph files in .cgm and then use a MS Word macro to load the .cgm files into MS Word file.

Now SAS has offered two new ways to generate the graphs: SASODS Graphics and SAS Stat Graphics. I had previously discussed SAS ODS Graphicswith template using Proc Lifetest to generate the Kaplan-Meier curve.

The procedure SGPLOT in SAS ODS Graphics can be used to generate high quality graph. With SGPLOT available, SAS/Graph procedure GPLOT become obsolete. The programs below illustrate the use of SGPLOT to generate the time-concentration curve for PK concentration data.

The graph generated from the programs is directly outputted in a .pdf file.

data pconc;
  input drug $ time concentration;
datalines;
Test  0   0
Test  1  32
Test  3 100
Test  6 140
Test  12 70
Test  24 25
Test  48  5
Test  72  1
Ref   0   0
Ref   1  40
Ref   3 105
Ref   6 135
Ref  12  80
Ref  24  28
Ref  48   7
Ref  72   0
;

options orientation=landscape nodate;
ods graphics / reset width=9in noborder;
    *width option defines the size of the graph. Noborder option remove  
     the border around the figure;

ods pdf file = "c:\temp\MySGPLOT.pdf"
 pdftoc = 0
 startpage = no
 style = printer
 dpi = 250;   

ods pdf nobookmarkgen;

title1 j=c "FIGURE #.#" ;
title2 j=c "MEAN PLASMA CONCENTRATION VS. TIME CURVES" ;
title3 j=c "BY TREATMENT" ;
title4 j=C "POPULATION:  PK" ;

footnote1 j=l "%upcase(PROGRAM: xxx.sas)                                                                                                                 (&sysdate &systime)";
run ;

proc sgplot data=pconc ;
    series x = time y = concentration
         /group=drug lineattrs = (thickness = 1 pattern=solid) markers;
    xaxis label = 'Time Post Study Drug Administration (hours)' 
         grid values = (0 to 80 by 5);
    yaxis label = 'Mean Concentration' grid values = (0 to 160 by 40); 
    keylegend / location=inside position=topright;
run;


ods pdf close;






















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