Application of pharmacometric approaches to evaluate effect of weight and renal function on pharmacokinetics of alogliptin

• Published in: British journal of clinical pharmacology Vol. 81; no. 4; pp. 700 - 712
• Main Authors: Naik, Himanshu, Czerniak, Richard, Vakilynejad, Majid
• Format: Journal Article
• Language: English
• Published: England John Wiley and Sons Inc 01.04.2016
• Subjects: Adolescent; Adult; Aged; alogliptin; Biological Availability; Body Weight; Clinical Trials, Phase I as Topic; Clinical Trials, Phase III as Topic; Diabetes Mellitus, Type 2 - blood; Diabetes Mellitus, Type 2 - drug therapy; Dose-Response Relationship, Drug; DPP‐4 inhibitor; Female; Humans; Hypoglycemic Agents - administration & dosage; Hypoglycemic Agents - blood; Hypoglycemic Agents - pharmacokinetics; Hypoglycemic Agents - therapeutic use; Kidney - metabolism; Kidney Function Tests; Male; Middle Aged; Models, Biological; Pharmacokinetics; Piperidines - administration & dosage; Piperidines - blood; Piperidines - pharmacokinetics; Piperidines - therapeutic use; renal function; Tissue Distribution; type 2 diabetes mellitus; Uracil - administration & dosage; Uracil - analogs & derivatives; Uracil - blood; Uracil - pharmacokinetics; Uracil - therapeutic use; Young Adult; type 2 diabetes mellitus; renal function; DPP-4 inhibitor; alogliptin
• ISSN: 0306-5251; 1365-2125
• DOI: 10.1111/bcp.12853

Aims The aims of the study were to characterize the pharmacokinetics (PK) of alogliptin in healthy and type 2 diabetes mellitus (T2DM) subjects using a population PK approach and to assess the influence of various covariates on alogliptin exposure. Methods Plasma concentration data collected from two phase 1 studies and one phase 3 study following administration of alogliptin (12.5–400 mg) were used for the PK model development. One‐ and two‐compartment models were evaluated as base structural PK models. The impact of selected covariates was assessed using stepwise forward selection and backward elimination procedures. The predictability and robustness of the final model was evaluated using visual predictive check and bootstrap analyses. The final model was used to perform simulations and guide appropriate dose adjustments. Results A two‐compartment model with first‐order absorption and elimination best described the alogliptin concentration vs. time profiles. Creatinine clearance and weight had a statistically significant effect on the oral clearance (CL/F) of alogliptin. The model predicted a lower CL/F (17%, 35%, 80%) and a higher systemic exposure (56%, 89%, 339%) for subjects with mild, moderate and severe renal impairment, respectively, compared with healthy subjects. Effect of weight on CL/F was not considered clinically relevant. Simulations at different doses of alogliptin support the approved doses of 12.5 mg and 6.25 mg for patients with moderate and severe renal impairment, respectively. Conclusions The PK of alogliptin was well characterized by the model. The analysis suggested an alogliptin dose adjustment for subjects with moderate‐to‐severe renal impairment and no dose adjustments based on weight.