Translational PK/PD and model-informed development of JNJ-67842125, a Fab reversal agent for JNJ-64179375, a long-acting thrombin inhibitor

Mechanistic Pharmacokinetics and Pharmacodynamics of GalNAc-siRNA: Translational Model Involving Competitive Receptor-Mediated Disposition and RISC-Dependent Gene Silencing Applied to Givosiran

Triantennary N-acetyl-D galactosamine (GalNAc)3-conjugated small interfering RNA (siRNA) have majorly advanced the development of RNA-based therapeutics. Chemically stabilized GalNAc-siRNAs exhibit extensive albeit capacity-limited (nonlinear) distribution into hepatocytes with additional complexities in intracellular liver disposition and pharmacology. A mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) model of GalNAc-siRNA was developed to i) quantitate ASGPR-mediated disposition and downstream RNA-induced silencing complex (RISC)-dependent pharmacology following intravenous (IV) and subcutaneous (SC) dosing, ii) assess the kinetics of formed active metabolite, iii) leverage, as an example, published experimental data for givosiran, and iv) demonstrate PK translation across two preclinical species (rat and monkey) with subsequent prediction of human plasma PK. The structural model is based on competition between parent and formed active metabolite for occupancy and uptake via ASGPR into hepatocytes, intracellular sequestration and degradation, and downstream engagement of RNA-induced silencing complex (RISC) governing target mRNA degradation. The model jointly and accurately captured available concentration-time profiles of givosiran and/or AS(N-1)3' givosiran in rat and/or monkey plasma, liver, and/or kidney following givosiran administered both IV and SC. RISC-dependent gene silencing of ALAS1 mRNA was well-characterized. The model estimated an in vivo affinity (KD) value of 27.7 nM for GalNAc-ASGPR and weight-based allometric exponents of -0.27 and -0.24 for SC absorption and intracellular (endolysosomal) degradation rate constants. The model well-predicted reported givosiran plasma PK profiles in humans. PK simulations revealed net-shifts in liver-to-kidney distribution ratios with increasing IV and SC dose. Importantly, decreases in the relative liver uptake efficiency were demonstrated following IV and, to a lesser extent, following SC dosing explained by differential ASGPR occupancy profiles over time.

Translational Pharmacokinetic/Pharmacodynamic Modeling and Simulation of Oxaliplatin and Irinotecan in Colorectal Cancer

Despite the recent advances in this field, there are limited methods for translating organoid-based study results to clinical response. The goal of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model to facilitate the translation, using oxaliplatin and irinotecan treatments with colorectal cancer (CRC) as examples. The PK models were developed using qualified oxaliplatin and irinotecan PK data from the literature. The PD models were developed based on antitumor efficacy data of SN-38 and oxaliplatin evaluated in vitro using tumor organoids. To predict the clinical response, translational scaling of the models was established by incorporating predicted ultrafiltration platinum in plasma or SN-38 in tumors to PD models as the driver of efficacy. The final PK/PD model can predict PK profiles and responses following treatments with oxaliplatin or irinotecan. After generation of virtual patient cohorts, this model simulated their tumor shrinkages following treatments, which were used in analyzing the efficacies of the two treatments. Consistent with the published clinical trials, the model simulation suggested similar patient responses following the treatments of oxaliplatin and irinotecan with regards to the probabilities of progression-free survival (HR = 1.05, 95%CI [0.97;1.15]) and the objective response rate (OR = 1.15, 95%CI [1.00;1.32]). This proposed translational PK/PD modeling approach provides a significant tool for predicting clinical responses of different agents, which may help decision-making in drug development and guide clinical trial design.

Prediction of relative change in free nerve growth factor following subcutaneous administration of tanezumab, a novel monoclonal antibody to nerve growth factor

Tanezumab is a monoclonal antibody against nerve growth factor (NGF). We investigated tanezumab pharmacokinetic (PK)-NGF relationships and predicted the extent of systemic free NGF suppression with target-mediated drug disposition (TMDD) modeling using data from three pivotal phase III interventional studies (NCT02697773, NCT02709486, and NCT02528188) in patients with osteoarthritis. Patients received tanezumab 2.5 mg or 5 mg every 8 weeks (q8w) subcutaneously. A TMDD model using a previously established population PK model was used to describe plasma tanezumab and serum total NGF concentration data, and simulations were performed to predict "unobserved" free NGF versus time profiles and dose-response relationships for free NGF. A total of 2992 patients had available data for plasma tanezumab or serum total NGF concentrations and were included in the analysis; 706 of these had data for both tanezumab and total NGF concentrations. The model generally performed well to predict observed total NGF concentrations up to ~24 weeks after each dose. Simulations suggested free NGF concentration would be suppressed by ~75% (median) near the peak of tanezumab concentration and by less than 5% (median) around the trough tanezumab concentration with a tanezumab 2.5 mg q8w regimen. Free NGF concentration was predicted to return to baseline level at ~8 weeks (95% prediction interval: 5-16 weeks) after the last tanezumab dose. This model adequately described plasma tanezumab and serum total NGF concentrations following s.c. administration of tanezumab 2.5 or 5 mg q8w, allowed prediction of relative change in systemic free NGF following s.c. administration of tanezumab.

Minimal Physiologically-Based Pharmacokinetic (mPBPK) Metamodeling of Target Engagement in Skin Informs Anti-IL17A Drug Development in Psoriasis

The pharmacologic effect(s) of biotherapeutics directed against soluble targets are driven by the magnitude and duration of free target suppression at the tissue site(s) of action. Interleukin (IL)-17A is an inflammatory cytokine that plays a key role in the pathogenesis of psoriasis. In this work, clinical trial data from two monoclonal antibodies (mAbs) targeting IL-17A for treatment of psoriasis (secukinumab and ixekizumab) were analyzed simultaneously to quantitatively predict their target engagement (TE) profiles in psoriatic skin. First, a model-based meta-analysis (MBMA) for clinical responses was conducted separately for each drug based on dose. Next, a minimal physiologically-based pharmacokinetic (mPBPK) model was built to assess skin site IL-17A target engagement for ixekizumab and secukinumab simultaneously. The mPBPK model captured the observed drug PK, serum total IL-17A, and skin drug concentration-time profiles reasonably well across the different dosage regimens investigated. The developed mPBPK model was then used to predict the average TE (i.e., free IL-17A suppression) in skin achieved over a 12-weeks treatment period for each drug following their respective regimens and subsequently assess the TE-efficacy response relationship. It was predicted that secukinumab achieved 98.6% average TE in the skin at 300 mg q4w SC while ixekizumab achieved 99.9% average TE under 160 mg (loading) followed by 80 mg q2w SC. While direct quantification of free IL-17A levels at the site of action is technically challenging, integrated mPBPK-MBMA approaches offer quantitative predictions of free IL-17A levels at the site of action to facilitate future drug development via IL-17A suppression in psoriasis.