# Presentation: 724364

## Prevalidation and Validation Study Plan for Minced Testes Assay

- Gary Timm
- Presented to EDMVS
- June 4, 2003

## Objectives

- To assess relevance of minced testes assay for detecting compounds that affect steroidogenesis
- Measure change in testosterone production relative to controls
- To assess reliability
- Measure variability in testosterone measurements of participating laboratories
- Measure variability in mean response among participating laboratories

## Data Interpretation 

- Assay will detect  interference with key steps in the steroidogenic pathway:
- Decreases in steroidogenic signal transduction
- Interference with the transport of cholesterol from the cytoplasm to the mitochondria via StAR
- Inhibition of enzymes involved in the conversion of cholesterol to testosterone

## Testis

- _Testis_

- _Ovary_

- ThecaCells

- (Females)

- GranulosaCells

- Testis andPeripheralTissues

- LeydigCells

- _Intracellular Compartment_

- CorpusLuteum

- Mitochondria

- Cytoplasm

## Data Interpretation (2)

- Interference with steroidogenesis will result in a decrease or increase in measured testosterone relative to controls
- Assay is unlikely to detect
- Inhibition of aromatase
- 5α-reductase inhibitors
- Inhibition of these downstream steps would result in an increase in testosterone production

## Basic Protocol

- Optimization study will determine parameters of protocol
- Replicate Runs
- 3 with hCG stimulation
- 3 without hCG stiulation
- Dosing
- 3 dose levels
- 1 positive control
- Media control
- Sample 3-4 time points

## Prevalidation Studies

- Purpose:
- To obtain initial information on protocol transferability
- Primary test of relevance
- Studies:
- Protocol optimization study
- Baseline study
- Pilot study
- Multichemical study

## Prevalidation Studies

- Optimization of minced testes protocol has been completed
- Baseline study
- Two labs to run optimized protocol
- 3 runs without hCG
- 3 runs with hCG challenge
- Measure testosterone formation and LDH
- No test chemical
- 3 replicates

## Prevalidation Studies (2)

- Pilot studies
- Aminoglutethimide (positive control)
- Ethane dimethanesulfonate (Leydig cell toxicant)
- Two labs
- Three replicates
- Multichemical studies
- 9 challenge chemicals
- Two labs
- Two replicates

## Selection of Reference Chemicals

- Selected for known mode of action
- Limited by availability
- Pharmaceuticals are difficult at best to procure because the require material transfer agreements
- Many pharmaceuticals are not available from the manufacturer
- Thus, we will have to duplicate many of the chemicals in  validation that were used in prevalidation, if we want to cover modes of action

## Reference Chemicals

| Chemical | Mode 1 | Mode 2 | Preval | Val |
| --- | --- | --- | --- | --- |
| Aminogluteth
imide | P450scc | Aromatase | + Cont | + Cont |
| Bisphenol A | Inhibits
C-AMP |  |      X |      |
| Dimethoate  | StAR inhibitor |  |      X |      X |
| EDS | Leydig cell toxicant |  |      X |      X |
| Fenarimol | Aromatase |  |      X |       |

## Reference Chemicals

| Chemical | Mode 1 | Mode 2 | Preval | Val |
| --- | --- | --- | --- | --- |
| Flutamide | P450c17 |  |      X |      |
| Genistein | 3β-HSD inhibitor |  |      X |     X |
| Ketoconazole | P450scc | Aromatase |      X |  |
| Lindane | Inhibits
C-AMP |  |  |     X |
| MK-434 | 5α reductase |  |      ? |      |
| Vinclozolin | Negative chemical |  |      X |     X |

## Selection of Laboratories

- Laboratories to be selected by the Contractor by open solicitation
- Laboratories must be:
- Independent
- Experienced in:
- In vitro test methods
- Cell and tissue culturing
- Test chemical administration
- Enzyme kinetics and inhibition studies
- Knowledge of steroidogenesis
- Compliance with GLP

## Measurements of Reliability

- Coefficient of variation across studies
- Study standard deviation/mean of studies
- Reflects the spread among study means in relation to their average value
- Ratio of between- to within-study standard deviation
- Standard deviation across studies/average standard error within studies
- Reflects relative contribution to total variation of the variability among study means as compared to the precision within studies
- Comparison of within-lab SD to Average within-lab SD
- Standard deviation of lab I/ geometric mean within-lab SD
- Measures the homogeneity of within study variation across laboratories
- Can identify poor performing labs

## Determination of Number of Laboratories

- Sensitivity analysis for each measure of reliability was prepared using literature values. (Fail, Gray Laskey)
- For Criterion 1 (CV interval factor):
- 95% confidence interval factors were calculated as a function of the number of laboratories and the number of replicate determinations per laboratory.
- A 95% confidence interval on the characteristic of interest is calculated by multiplying the point estimate by the confidence interval factor.
- Confidence interval factor is sensitive to the number of labs and approaches 1 as the number of labs increases, flattens after ~8 labs
- Criterion 1 is not sensitive to the number of replicate determinations per lab

## Determination of Number of Laboratories (2)

- Criterion 2 (Lower and Upper confidence interval factor of between:within SD)
- 95% confidence interval factors were calculated as a function of the number of laboratories and the number of replicate determinations per laboratory
- Lower confidence interval is sensitive to both number of labs and number of replicates
- Upper confidence level is sensitive only to the number of labs
- Flattens out after ~8 labs

## Determination of Number of Laboratories (3)

- Criterion 3 (Lower confidence factor of within- laboratory standard deviations to average within- laboratory standard deviation)
- 95% confidence interval factors were calculated as a function of the number of laboratories and the number of replicate determinations per laboratory
- Sensitive to number of replicates
- Not sensitive to number of labs
- Flattens after ~8 replicates

## Determination of Number of Laboratories (4)

- Conclusions
- Based on available data, 6-10 laboratories are needed to achieve a high confidence indication of assay reliability
- We shall select 6 laboratories as the actual variability in these studies should be less than in the literature where different protocols were used
- ~8 replicates are needed to obtain a high confidence estimate of within laboratory standard deviations.  This information will be generated by the positive and negative controls.

## Validation

** ****6 labs **
**Baseline studies**
- 3 runs without hCG
- 3 runs with hCG challenge
- Measure testosterone formation and LDH
- No test chemical
- Two replicates
**Pilot studies**
- Aminoglutethimide (positive control)
- Ethane dimethanesulfonate (Leydig cell toxicant)
- Two replicates

## Validation

**Coded sample studies**
- 5 chemicals
- 2 replicates per laboratory
- Aminogluthethimide is positive control
- Vinclozolin (AR antagonist) used as negative chemical
- May modify validation study plan based on results of prevalidation work

## Reference Chemicals by Mode of Action

| Mode of Action | Prevalidation | Validation |
| --- | --- | --- |
| C-AMP inhibitor | BPA | Lindane |
| StAR inhibitor | Dimethoate | Dimethoate |
| P450scc | + Cont Ketoconazole | + Cont |
| P450c17 | Flutamide |  |
| 3β-HSD  | Genestein | Genestein |
| 5α reductase | MK-434 |  |
| Aromatase | Fenarimol |  |
| Negative | Vinclozolin | Vinclozolin |

## Data Analysis

-    Intra-Laboratory Analysis
- Assess chemically related testosterone inhibition within laboratories
-    Inter-Laboratory Analysis
- Assess extent of heterogeneity of chemical inhibition effects across laboratories

## Data Analysis Strategy

- Large numbers of comparisons can be identified, for both intra and inter laboratory analyses
- Baseline studies
- Preval: 2 labs x 3 replicates = 6
- Validation: 6 labs x 2 replicates = 12
- Positive Control (aminoglutethimide)
- Preval pilot: 2 labs x 3 replicates = 6
- Validation pilot: 6 labs x 2 replicates = 12
- Preval high dose: 2 labs x 9 chems x 2 replicates = 36
- Validation high dose: 6 labs x 5 chems x 2 reps = 60

## Data Analysis Strategy (2)

**Preval/val chemicals**
- 2 labs x 4 chems x 2 replicates = 16
- 6 labs x 4 chems x 2 replicates = 48
**Preval chemicals**:  2 labs x 5 chems x 2 replicates = 20
**Validation chemicals:**  6 labs x 1 chem x 2 replicates = 12
- Total of 228 studies
- In validation study carry out each analysis considered *a priori* to be possibly toxicologically relevant
- Make recommendations in final report concerning which analyses are most informative and so should be included in assay standard practice

## Intra-Laboratory Analysis

- Principal Endpoints
- Cumulative Testosterone Concentration
- Cumulative LDH Concentration
- (3 doses and 3-4 time points)
- Similar analyses for both endpoints
- Variation in Effects
- Across chemicals
- Across graded chemical doses

## Components of Variation

- Rat-to-rat
- Testis-to-testis within rat
- Fragment-to-fragment within testis
- Block of assays performed simultaneously (e.g. day-to-day)
- Variance components need to be accounted for in the statistical analysis

## Inter-Laboratory Analysis

- Focus on primary and secondary responses from intra-laboratory analysis
- Assess extent of heterogeneity of responses across laboratories
- Reference
- American Society for Testing and Materials (1988).  “Standard Practice for Conducting an Inter-Laboratory Study to Determine the Precision of a Test Method”

## Measures of Variation Among Laboratories

- Heterogeneity of within-laboratory means across laboratories
- Heterogeneity of within-laboratory standard deviations across laboratories
- Ratio of laboratory-to-laboratory standard deviation to average within-laboratory standard deviation
- Coefficient of variation across laboratories

## Reporting

- Each laboratory will report:
- That protocol was followed
- Difficulties in executing the studies
- Summary of data
- Raw data
- Validation Study Report

## Graphical Summary Displays

- Prepare control charts and associated control limits
- Display intra-laboratory statistics side-by-side across
- Chemicals
- Graded doses
- Laboratories
- Identify outlying laboratories and nature of discrepancies