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Aim To assess the potential of interleukin-6 (IL-6) signaling blockade in the lung to treat SARS-CoV-2 infection via model-based simulation by exploring soluble IL-6 receptor (sIL-6R) sequestration by tocilizumab (TCZ) and IL-6 sequestration by siltuximab (SIL). Methods Literature values of IL-6, IL-6 antagonist SIL, sIL-6R, IL-6R antagonist TCZ, and their respective binding constants were used to develop a model to predict the impact of treatment on IL-6 signaling. Models were used to generate simulated bronchoalveolar lavage fluid (BALF) concentrations for normal subjects, subjects at risk of developing acute respiratory distress syndrome (ARDS), and subjects with ARDS under four conditions: without treatment, treatment with TCZ, treatment with SIL, and treatment with TCZ + SIL. Results With TCZ intervention, IL-6 levels are unaffected and sIL-6R is reduced somewhat below the Normal case. IL-6:sIL-6R complex only slightly decreased relative to the no-intervention case. With SIL intervention, sIL-6R levels are unaffected and IL-6 is greatly reduced below the Normal case. IL-6:sIL-6R complex is greatly decreased relative to the no-intervention case. With TCZ + SIL intervention, IL-6 and sIL-6R levels are reduced below the Normal case and achieve suppression equivalent to monotherapy results for their respective targets. IL-6:sIL-6R complex reduction is predicted to be greater than that achieved with monotherapy. This reflects sequestration of both components of the complex and the nonlinear binding equilibrium. Conclusion Co-administration of both IL-6 and IL-6R sequestering products such as SIL and TCZ may be necessary to effectively treat COVID-19 patients who have or are at risk of developing ARDS.

AVB-S6-500 neutralized growth arrest-specific 6 (GAS6) protein and effectively inhibited AXL signaling in preclinical cancer models. A target-mediated drug disposition (TMDD) pharmacokinetic/pharmacodynamic (PK/PD) model was used to select first-in-human (FIH) doses for AVB-S6-500 based on predicted target (GAS6) suppression in the clinic. The effect of TMDD on AVB-S6-500 clearance was incorporated into a standard two-compartment model, providing parallel linear and nonlinear clearance. Observed AVB-S6-500 and GAS6 concentration data in cynomolgus monkeys and relevant interspecies differences were used to predict the PK (serum concentration)/PD (GAS6 suppression) relationship in humans. Human exposure and GAS6 suppression were simulated for the proposed FIH doses of 1, 2.5, 5, and 10 mg/kg. A dose of 1 mg/kg was selected to target GAS6 suppression for 2 weeks in the initial healthy volunteer study. The cynomolgus monkey:human ratios for the highest proposed FIH dose were anticipated to yield more than a 10-fold margin to the nonclinical no observed adverse event level while maintaining > 90% GAS6 suppression. In human subjects, the first dose (1 mg/kg) model-projected and clinically observed maximal concentration (Cmax ) was within 10% of predicted; repeat dosing at 5 mg/kg was within 1% (Cmax ) and 45% (area under the serum concentration-time curve from time 0 to end of dosing interval) of predicted. Predicted GAS6 suppression duration of 14 days was accurate for the 1 mg/kg dose. A PK/PD model expedited clinical development of AVB-S6-500, minimized exposure of patients with cancer to subtherapeutic doses, and rationally guided the optimal dosing in patients.

During a pandemic caused by a novel pathogen (NP), drug repurposing offers the potential of a rapid treatment response via a repurposed drug (RD) while more targeted treatments are developed. Five steps of model-informed drug repurposing (MIDR) are discussed: (i) utilize RD product label and in vitro NP data to determine initial proof of potential, (ii) optimize potential posology using clinical pharmacokinetics (PK) considering both efficacy and safety, (iii) link events in the viral life cycle to RD PK, (iv) link RD PK to clinical and virologic outcomes, and optimize clinical trial design, and (v) assess RD treatment effects from trials using model-based meta-analysis. Activities which fall under these five steps are categorized into three stages: what can be accomplished prior to an NP emergence (preparatory stage), during the NP pandemic (responsive stage) and once the crisis has subsided (retrospective stage). MIDR allows for extraction of a greater amount of information from emerging data and integration of disparate data into actionable insight.

Understanding the pharmacokinetic (PK) and pharmacodynamic (PD) relationship of a therapeutic monoclonal antibody against proprotein convertase subtilisin/kexin type 9 (PCSK9) exhibiting target‐mediated drug disposition (TMDD) is critical for selecting optimal dosing regimens. We describe the PK/PD relationship of evolocumab using a mathematical model that captures evolocumab binding and removal of unbound PCSK9 as well as reduction in circulating low‐density lipoprotein cholesterol (LDL‐C). Data were pooled from 2 clinical studies: a single‐dose escalation study in healthy subjects (7‐420 mg SC; n = 44) and a multiple‐dose escalation study in statin‐treated hypercholesterolemic patients (14 mg weekly to 420 mg monthly [QM] SC; n = 57). A TMDD model described the time course of unbound evolocumab concentrations and removal of unbound PCSK9. The estimated linear clearance and volume of evolocumab were 0.256 L/day and 2.66 L, respectively, consistent with other monoclonal antibodies. The time course of LDL‐C reduction was described by an indirect response model with the elimination rate of LDL‐C being modulated by unbound PCSK9. The concentration of unbound PCSK9 associated with half‐maximal inhibition (IC50) of LDL‐C elimination was 1.46 nM. Based on simulations, 140 mg every 2 weeks (Q2W) and 420 mg QM were predicted to achieve a similar time‐averaged effect of 69% reduction in LDL‐C in patients on statin therapy, suggesting that an approximate 3‐fold dose increase is required for a 2‐fold extension in the dosing interval. Evolocumab dosing regimens of 140 mg Q2W or 420 mg QM were predicted to result in comparable reductions in LDL‐C over a monthly period, consistent with results from recently completed phase 3 studies.

Phase I studies have shown that AMG 151 activates glucokinase, a key enzyme in glucose homeostasis. The present randomized, placebo-controlled phase IIa study evaluated the dose-effect relationship of the glucokinase activator AMG 151 relative to placebo on fasting plasma glucose (FPG) in 236 patients (33-35 patients per arm) with type 2 diabetes treated with metformin. Patients received oral AMG 151 at 50, 100 or 200 mg twice daily, AMG 151 at 100, 200 or 400 mg once daily or matching placebo for 28 days. A significant linear dose-effect trend was observed with the twice-daily regimen (p = 0.004) for change in FPG to day 28. No trend was observed with the once-daily regimen. A higher incidence of hypoglycaemia and hypertriglyceridaemia was observed with AMG 151 administration. AMG 151 significantly reduced FPG when administered twice daily but not when administered once daily in patients with type 2 diabetes treated with metformin.

A computer model capable of integrating mechanisms of biofilm resistance to disinfection by antimicrobial agents was developed. Resistance mechanisms considered included retarded penetration due to a stoichiometric reaction between the antimicrobial agent and biomass, incomplete penetration due to a catalytic reaction between the antimicrobial agent and the biomass, and the existence of a fraction of the cells in a resistant phenotypic state. Mathematical models of these processes were derived and solved in the computer simulation package MATLAB. Four sets of fitted experimental data on the disinfection of Pseudomonas aeruginosa biofilms were fit to each of the three models. No one model fit all of the data sets adequately. Killing of a 2-day old biofilm by tobramycin was best described by the physiological limitation model. Killing by hypochlorite was best described by the stoichiometric transport model. Killing by hydrogen peroxide was best simulated by the catalytic transport model. These results suggest that multiple mechanisms of biofilm reduced susceptibility are manifested even in biofilms of the same species and that the particular resistance mechanism depends on the biofilm age, antimicrobial agent, and biofilm thickness. The models presented in this article may be useful for diagnosing mechanisms of biofilm resistance from experimental data.

Clinical trial simulation (CTS) and model-based meta-analysis (MBMA) can increase our understanding of small, first-in-patient (FIP) trial design performance to inform Phase 2 decision making. In this work, we compared dose-ranging designs vs. designs testing only placebo and the maximum dose for early decision making in psoriasis. Based on MBMA of monoclonal antibodies in the psoriasis space, a threshold of greater than a 50 percentage point improvement over placebo effect at the highest feasible drug dose was required for the advancement in psoriasis. Studies testing only placebo and the maximum dose made the correct advancement decision marginally more often than dose-ranging designs in the majority of the cases. However, dose-ranging studies in FIP trials offer important design advantages in the form of dose-response (D-R) information to inform Phase 2 dose selection. CTS can increase the efficiency and quality of drug development decision making by studying the limitations and benefits of study designs prospectively.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e58; doi:10.1038/psp.2013.32; published online 24 July 2013.