Human ADME/PK is lost in translation and prediction from in silico to in vitro to in vivo
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Background
Measurements and predictions of aqueous solubility (S), apparent cell permeability (P app ), unbound microsomal intrinsic clearance (CL int,u ), unbound fraction in plasma (f u ), log D, Lipinski’s Rule of 5 (Ro5) and BBB+/BBB- (blood-brain-barrier) are commonly used in early drug discovery to evaluate whether compounds are likely to have adequate ADME/PK in humans. The main objective was to evaluate the validity and proposed thresholds for commonly used in silico to in vitro (with particular interest in the ADMET Predictor software) and in vitro to in vivo human ADME/PK prediction methods, and physicochemical estimates and rules of thumb. A secondary aim was to compare validity and thresholds to that for the prediction software ANDROMEDA.
Methods
Data were collected from literature and own studies. Main measures of validity were Q 2 (true predictive accuracy; in silico models), R 2 (correlation coefficient; in vitro models), Q 2 • R 2 (for translation from in silico to in vivo via in vitro ), skewness, range, % correct predicted class and clinical relevance.
Results and Discussion
Poor accuracies (Q 2 • R 2 =0.05, 0.05, 0.36 and 0.45; ∼0 at low to moderate, decision-critical levels) and class predictions, limited ranges (not covering low to moderate estimates), systematic errors (often considerable overprediction at low values), poor clinical relevance and inadequate thresholds were found. Predictive accuracy was mainly lost in the translation from in vitro to in vivo . Log D and Ro5 are poor predictors of oral bioavailability and half-life. Ro5 produced 63 % false negatives for prediction of poor/good oral absorption. The overall mean Q 2 for ANDROMEDA was 3 times higher (0.57 vs 0.2). Advantages with ANDROMEDA compared to in vitro data-based in silico models include wider application domain (high resolution at low values), extrahepatic elimination models, minimal skewness, clinical relevance, balanced thresholds, and compound- and parameter-unique confidence intervals. ANDROMEDA successfully predicted the human clinical ADME/PK for all small drugs marketed in 2021, while the in silico to in vitro to in vivo approach was out of reach for all.
Conclusion
The validity of investigated methodologies (including ADMET Predictor) and thresholds were overall very low. Unless both predicted S, permeability and f u are high and CL int is moderate (an overall criterium not met for investigated modern small drugs) one is more or less lost in the translation, and will jeopardize compound selection and optimization. This clearly shows the need for better and thoroughly validated prediction models and software. Marked improvements in accuracy, range, balance and clinical relevance were achieved with ANDROMEDA, which predicts human clinical ADME/PK directly from chemical structures and has undergone extensive validation.
