Impact of Physiologically Based Pharmacokinetics, Population Pharmacokinetics and Pharmacokinetics/Pharmacodynamics in the Development of Antibody‐Drug Conjugates

Abstract

Antibody-drug conjugates are important molecular entities in the treatment of cancer, with 8 antibody-drug conjugates approved by the US Food and Drug Administration since 2000 and many more in early-and late-stage clinical development. These conjugates combine the target specificity of monoclonal antibodies with the potent anticancer activity of small-molecule therapeutics. The complex structure of antibody-drug conjugates poses unique challenges to pharmacokinetic (PK) and pharmacodynamic (PD) characterization because it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (eg, conjugate, total antibody, and unconjugated payload) in different tissues. Quantitative clinical pharmacology using mathematical modeling and simulation provides an excellent approach to overcome these challenges, as it can simultaneously integrate the disposition, PK, and PD of antibody-drug conjugates and their components in a quantitative manner. In this review, we highlight diverse quantitative clinical pharmacology approaches, ranging from system models (eg, physiologically based pharmacokinetic [PBPK] modeling) to mechanistic and empirical models (eg, population PK/PD modeling for single or multiple analytes, exposure-response modeling, platform modeling by pooling data across multiple antibody-drug conjugates). The impact of these PBPK and PK/PD models to provide insights into clinical dosing justification and inform drug development decisions is also highlighted.

Risk‐Based Pharmacokinetic and Drug–Drug Interaction Characterization of Antibody–Drug Conjugates in Oncology Clinical Development: An International Consortium for Innovation and Quality in Pharmaceutical Development Perspective

“…Given that it is not generally feasible to collect tissue samples routinely, we recommend closely monitoring toxicity (e.g., liver, kidney, or brain toxicities), as warranted, and do not generally advocate for conducting a dedicated clinical PK DDI study. It is worth noting that translational PBPK modeling that captures the tissue distribution and ADME characteristics of the ADC and release payload has been developed and reported for trastuzumab emtansine and valine‐citrulline‐monomethyl auristatin E ADCs 44–46 . Recently, Simcyp Simulator has adapted the ADC module based on the model structure developed for valine‐citrulline‐monomethyl auristatin E ADCs.…”

“…It is worth noting that translational PBPK modeling that captures the tissue distribution and ADME characteristics of the ADC and release payload has been developed and reported for trastuzumab emtansine and valine-citrulline-monomethyl auristatin E ADCs. [44][45][46] Recently, Simcyp Simulator has adapted the ADC module based on the model structure developed for valine-citrulline-monomethyl auristatin E ADCs. With the user-friendly interface, it is foreseeable that more applications of PBPK models will be seen in the near future, including using a PBPK model of ADCs to identify the highrisk organs to be monitored clinically for toxicity by projecting the potential DDI risk of released payload at tissue levels.…”

Pengenalan Sains Farmasi Sejak Dini (Perjalanan Obat di Dalam Tubuh) Kepada Anak-Anak Di Yayasan Sweet Shodaqoh Karawang

“…Aplikasi konsep farmakokinetika digunakan untuk menentukan besarnya dosis dan interval pemberian obat untuk individu sehingga diperoleh terapi yang rasional, yang meliputi cara bagaimana obat digunakan, berapa besarnya dosis dan interval pemberian, lama penggunaan, serta lama pengobatan [8]. Aplikasi konsep farmakokinetika merupakan salah satu pendekatan yang harus dilakukan untuk menghindari kemungkinan terjadinya efek toksik, meminimalkan efek samping obat, serta mengoptimalkan terapi [9,10]. Selain itu, tingkat kepatuhan pasien dalam meminum obat juga akan berpengaruh terhadap keberhasilan terapi [11,12].…”

Correction

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The name of author Matts Kågedal was incorrectly spelled as Matts Kaagedal in the published version. 1 This has now been fixed.

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