Building In Silico Models to Trigger Retesting: A Strategy on How to Use Predictive Models to Identify Potentially Incorrect In Vitro Intrinsic Clearance Results

Abstract

Abstract

Introduction:

The results from biological assays, such as microsomal intrinsic clearance, are often associated with moderate to high variability. Nevertheless, it is crucial to disciplines, such as drug discovery and toxicological risk assessment, to trust such experimental results. In the following study, a novel approach is suggested, which is based on in silico predictions and confidence scoring triggering experimental retesting.

Materials and Methods:

After successful validation of in silico models and confidence scoring, experiments with correct predictions (n = 73) and incorrect predictions (n = 65), both with high confidence scores (CS > 0.7), were repeated.

Results:

While 4.1% of the correct predictions changed their experimental outcome toward a different class, the incorrect predictions led to a class change in 27.7% of the experiments.

Discussion:

Such an in silico approach has the potential to identify inaccurate/variable results, which may then be subject to retesting. This suggested retesting strategy will improve decision-making and overall data quality if applied for a longer period. This may also then improve in silico models.

Conclusions:

As in silico models contribute toward improving in vitro data (due to adequate retesting), and higher data quality leads to more accurately predicting in silico models, this concept can be described as a virtuous circle of data quality.