Successful development of Biologics requires accurate prediction of human exposure. This is more challenging for biologics for which it is important to understand the characteristics of the affinity with the target. Due to the nonlinearity of exposure, simple allometric scaling models cannot be applied. Mechanistic-based models that quantitatively describe the drug–targeting interactions are needed for more accurate simulation and prediction.
TMDD is the phenomenon in which a drug binds with high affinity to its pharmacological target site (such as a receptor) to such an extent that this affects its pharmacokinetic (PK) characteristics.
The target binding and subsequent elimination of the drug-target complexes could affect both drug distribution and elimination, and result in nonlinearity of PK in a dose-dependent manner. This is most commonly observed as linear PK at high dose levels or high concentrations and nonlinear PK at low dose levels or low concentrations.
TMDD model structures are commonly determined by factors such as :
- drug dosing route (intravenous or not),
- drug distribution (number of compartments),
- location of the target (tissue or blood),
- and binding kinetics (fast or slow, high or low affinity, reversible binding or not, single target or not, elimination mechanisms, and others).
The concentration–time profiles of a given drug may not show all the phases, depending on the model structure and other factors including binding kinetics, target turnover rates, elimination of drug, and drug-target complex. Another common factor is the bioanalysis detection sensitivity. High detection limits may limit detection at the lower end of the profiles.
Each biologic drug candidate is unique in mechanism and performance, PhinC Development can help to determine when and how to use a TMDD model either for healthy subject PK prediction or for predicting PK in patients.