Quantification of Anti-Drug-Antibodies (ADA) Impact on Drug Exposure Using a Population PK modeling Approach
Candice Jamois (1), Johann Laurent (1), Gregor Lotz (2), Eginhard Schick (1), Meret Martin-Facklam (1), Valérie Cosson (1)
Roche Pharma Research and Early Development - Roche Innovation Center Basel (1) - Roche Innovation Center Munich (2)
Objectives: New engineered monoclonal antibodies (mAbs) represent effective therapeutic agents with high specificity for their targets-binding and efficient effector functions. However, the production of anti-drug Abs (ADAs) has become an important challenge during drug development as it may significantly influence the pharmacokinetics (PK), efficacy, and/or safety profiles of the mAbs [1]. The aim of this analysis is to quantify the impact of ADAs on clearance and exposure of Drug X using a population modeling approach, and to determine if a “dosing through” strategy [2] by increasing the dose is likely to succeed.
Methods: 1007 Drug X serum concentrations in 42 Phase 1 subjects receiving weekly IV doses (12.5 to 1500 mg) of drug X were analyzed using NONMEM. A 2-compartment PK models with time-dependent clearance (CL) and/or a time-dependent relative bioavailability (BIO) were tested to describe the Drug X PK in ADA- and ADA+ subjects. After validation by diagnostic plots and predictive check procedures, the model was used to quantify the impact of ADAs on exposure.
Results: 31% subjects receiving doses ≥ 150 mg developed ADAs leading to faster elimination of the drug. While sophisticated models (TMDD-like) [3] could describe the ADA-mediated clearance, simpler models with a time effect on parameters prove useful with better numerical properties. The final model is a 2 compartment PK model. The time dependent CL and BIO describe the effect of ADA on the PK of Drug X. Overall the parameters were adequately estimated except the time-effect on BIO for which RSE was >50%. Despite some (not yet resolved) misspecification of the model, it is nevertheless able to describe the observed PK profiles, and can be used to derive individual cumulative exposure (AUC). The ratio of AUC in ADA+ over AUC in ADA- subjects was computed after the 4th infusion. The ratio varies from 17% to 71% [150-1000 mg]. A trend towards a reduction of the impact of ADA on exposure is observed for doses ≥ 340 mg, suggesting that a “dosing through strategy” could work, assuming acceptable benefit/risk ratio of Drug X at higher doses.
Conclusions: A population PK model describing the PK profiles of Drug X in the presence or absence of ADAs was developed. Although some unresolved misspecification are present, the model is still useful to quantify the impact of ADAs on exposure and assess the efficiency of dosing through strategy during the conduct of a Phase I trial to evaluate.
References:
[1] J. M. Sailstad and al. A White Paper—Consensus and Recommendations of a Global Harmonization Team on Assessing the Impact of Immunogenicity on Pharmacokinetic Measurements. The AAPS Journal, Vol. 16, No. 3, May 2014.
[2] N. Chirmule and al. Immunogenicity to Therapeutic Proteins: Impact on PK/PD and Efficacy. The AAPS Journal, Vol. 14, No. 2, June 2012.
[3] X. Chen and al. A Mathematical Model of the Effect of Immunogenicity on Therapeutic Protein Pharmacokinetics. The AAPS Journal, Vol. 15, No. 4, October 2013.