Novel modelling approaches to predict the role of antivirals in reducing influenza transmission.

To aid understanding of the effect of antiviral treatment on population-level influenza transmission, we used a novel pharmacokinetic-viral kinetic transmission model to test the correlation between nasal viral load and infectiousness, and to evaluate the impact that timing of treatment with the antivirals oseltamivir or baloxavir has on influenza transmission. The model was run under three candidate profiles whereby infectiousness was assumed to be proportional to viral titer on a natural-scale, log-scale, or dose-response model. Viral kinetic profiles in the presence and absence of antiviral treatment were compared for each individual (N = 1000 simulated individuals); subsequently, viral transmission mitigation was calculated. The predicted transmission mitigation was greater with earlier administration of antiviral treatment, and with baloxavir versus oseltamivir. When treatment was initiated 12-24 hours post symptom onset, the predicted transmission mitigation was 39.9-56.4% for baloxavir and 26.6-38.3% for oseltamivir depending on the infectiousness profile. When treatment was initiated 36-48 hours post symptom onset, the predicted transmission mitigation decreased to 0.8-28.3% for baloxavir and 0.8-19.9% for oseltamivir. Model estimates were compared with clinical data from the BLOCKSTONE post-exposure prophylaxis study, which indicated the log-scale model for infectiousness best fit the observed data and that baloxavir affords greater reductions in secondary case rates compared with neuraminidase inhibitors. These findings suggest a role for baloxavir and oseltamivir in reducing influenza transmission when treatment is initiated within 48 hours of symptom onset in the index patient.

Mitigating Drug-Target-Drug Complexes in Patients With Paroxysmal Nocturnal Hemoglobinuria Who Switch C5 Inhibitors.

Drug-target-drug complexes (DTDCs) are phenomena newly observed in patients who switch from the complement component 5 (C5) inhibitor eculizumab to crovalimab, a novel, anti-C5 antibody in development for paroxysmal nocturnal hemoglobinuria (PNH), because these agents bind to different C5 epitopes. In Part 3 of the four-part, phase I/II COMPOSER study, 19 patients with PNH switching from eculizumab received 1,000-mg crovalimab intravenously, then subcutaneous maintenance doses from Day 8 (680 mg every 4 weeks (q4w), 340 mg every 2 weeks, or 170 mg every week). Crovalimab exposure was transiently reduced, and size-exclusion chromatography and crovalimab-specific enzyme-linked immunosorbent assays revealed DTDCs in all 19 patients' sera. Additionally, self-limiting mild to moderate symptoms suggestive of type III hypersensitivity reactions occurred in two patients. Mathematical modeling simulations of DTDC kinetics and effects of dosing on DTDC size distribution using Part 3 data predicted that increased crovalimab concentrations could reduce the proportion of large, slow-clearing DTDCs in the blood. A simulation-guided, optimized crovalimab regimen (1,000 mg intravenously; four weekly, subcutaneous 340-mg doses; then 680 mg q4w from Day 29) was evaluated in Part 4. Confirming the model's predictions, mean proportions of large DTDCs in patients who switched from eculizumab to this optimized regimen decreased by > 50% by Day 22, and target crovalimab concentrations were maintained. No type III hypersensitivity reactions occurred in Part 4. Optimizing crovalimab dosing thus reduced the proportion of large DTDCs, ensured adequate complement inhibition, and may improve safety. Model-based dosing optimization to mitigate DTDC formation offers a useful strategy for patients switching to novel antibody treatments targeting soluble epitopes.

Further Evaluation of Coproporphyrins as Clinical Endogenous Markers for OATP1B.

Coproporphyrins (CP-I and CP-III) in plasma are considered potential markers for assessing liver organic anion-transporting polypeptide transporter OATP1B activity and monitoring OATP1B-mediated drug-drug interactions (DDIs) in clinical settings. However, the effect of altered renal clearance (CLrenal ) on CP-I and CP-III plasma exposure has rarely been examined. Therefore, the purpose of this study is to further evaluate CP-I and CP-III as clinical endogenous markers for OATP1B activity and to investigate the impact of CLrenal on DDI assessments for the first time. In this study, 18 healthy participants were recruited to receive RO7049389 (a potential inhibitor of OATP1B) 800 mg twice daily for 6 days and a single dose of pitavastatin (a probe drug of OATP1B) before and after RO7049389 treatment. Plasma concentrations of pitavastatin, CP I, CP III, and the amounts of CP-I and CP-III excreted in urine were measured. Seventeen healthy participants completed the study. After multiple doses of RO7049389, the area under the plasma concentration-time curve from time 0 to 12 hours of pitavastatin increased 1.95-fold (90% confidence interval [CI], 1.58-2.41), while for CP-I and CP-III it increased 3.00-fold (90%CI, 2.35-3.82) and 2.84-fold (90%CI, 2.22-3.65), respectively. Concurrently, the CLrenal of CP-I decreased by 31% (90%CI, 23%-39%), and that of CP-III decreased by 70% (90%CI, 61%-77%). In conclusion, CP-I and CP-III in plasma display the potential to be applied as endogenous markers for the evaluation of OATP1B inhibition in clinical trials. While renal transporters contribute significantly to the CLrenal of CP-III, it would be better to investigate the impact of the CLrenal on plasma exposure of CP-III during clinical DDI assessments.

Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level.

Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. The incorporation of ML in modeling and simulation (M&S) has been eagerly anticipated, and in this perspective, we highlight examples in which ML and M&S approaches can be integrated as complementary parts of a clinical pharmacology workflow.

Prediction of the Optimal Dosing Regimen Using a Mathematical Model of Tumor Uptake for Immunocytokine-Based Cancer Immunotherapy.

Purpose: Optimal dosing is critical for immunocytokine-based cancer immunotherapy to maximize efficacy and minimize toxicity. Cergutuzumab amunaleukin (CEA-IL2v) is a novel CEA-targeted immunocytokine. We set out to develop a mathematical model to predict intratumoral CEA-IL2v concentrations following various systemic dosing intensities.Experimental Design: Sequential measurements of CEA-IL2v plasma concentrations in 74 patients with solid tumors were applied in a series of differential equations to devise a model that also incorporates the peripheral concentrations of IL2 receptor-positive cell populations (i.e., CD8+, CD4+, NK, and B cells), which affect tumor bioavailability of CEA-IL2v. Imaging data from a subset of 14 patients were subsequently utilized to additionally predict antibody uptake in tumor tissues.Results: We created a pharmacokinetic/pharmacodynamic mathematical model that incorporates the expansion of IL2R-positive target cells at multiple dose levels and different schedules of CEA-IL2v. Model-based prediction of drug levels correlated with the concentration of IL2R-positive cells in the peripheral blood of patients. The pharmacokinetic model was further refined and extended by adding a model of antibody uptake, which is based on drug dose and the biological properties of the tumor. In silico predictions of our model correlated with imaging data and demonstrated that a dose-dense schedule comprising escalating doses and shortened intervals of drug administration can improve intratumoral drug uptake and overcome consumption of CEA-IL2v by the expanding population of IL2R-positive cells.Conclusions: The model presented here allows simulation of individualized treatment plans for optimal dosing and scheduling of immunocytokines for anticancer immunotherapy. Clin Cancer Res; 24(14); 3325-33. ©2018 AACRSee related commentary by Ruiz-Cerdá et al., p. 3236.

Population pharmacokinetic/pharmacodynamic model for C.E.R.A. in both ESA-naive and ESA-treated chronic kidney disease patients with renal anemia.

This study aimed to develop a population pharmacokinetic/pharmacodynamic (PK/PD) model for C.E.R.A., a continuous erythropoietin receptor activator. C.E.R.A. is administered via intravenous (IV) and subcutaneous (SC) routes once every 2 weeks (Q2W) or once every 4 weeks (Q4W), respectively, to correct or maintain hemoglobin levels in chronic kidney disease (CKD) patients. Population models were specified to describe C.E.R.A. (PK) and hemoglobin (PD) concentrations over time, using data from 3 phase III, open-label, randomized, parallel-group, multicenter studies that examined IV or SC C.E.R.A. administration Q2W and Q4W in erythropoiesis-stimulating agent (ESA)-naive and ESA-treated patients. C.E.R.A. PK was described by a 1-compartment model: drug clearance = 0.75 L/d, volume of distribution = 4.72 L, and half-life = 105 hours in accordance with previous reported values. The PD model, a life span sequential PK/PD model, adequately described hemoglobin data. Dosing schedule, administration route, and study type did not affect drug-related PD parameters or system-specific parameters (eg, red blood cell life span). This model adequately described C.E.R.A.'s PK and PD properties according to C.E.R.A. posology, thus permitting simulations exploring alternative drug administration scenarios. It supports use of C.E.R.A. IV and SC; Q2W for anemia correction in ESA-Naïve CKD patients and monthly administration in the hemoglobin maintenance phase.

The effects of CYP3A4 inhibition on erlotinib pharmacokinetics: computer-based simulation (SimCYP) predicts in vivo metabolic inhibition.

BACKGROUND: Erlotinib is an orally active antitumor agent. Analyses in vitro using human liver microsomes and recombinant enzymes showed that erlotinib was metabolized primarily by CYP3A4, with a secondary contribution from CYP1A2. METHODS: A computer-based simulation model, SimCYP, predicted that CYP3A4 contributed to approximately 70% of the metabolic elimination of erlotinib, with CYP1A2 being responsible for the other approximately 30%. A drug-drug interaction study was therefore conducted for erlotinib and a potent CYP3A4 inhibitor, ketoconazole, in healthy male volunteers to evaluate the impact of CYP3A4 inhibition on erlotinib exposure. RESULTS: Ketoconazole caused an almost two-fold increase in erlotinib plasma area under the concentration curve and in maximum plasma concentration. This is consistent with the SimCYP prediction of a two-fold increase in erlotinib AUC, further validating a primary (approximately 70%) role of CYP3A4 in erlotinib elimination. CONCLUSION: Prediction of clinically important drug-drug interaction with SimCYP using in vitro human metabolism data can be a powerful tool during early clinical development to ensure safe administration of anticancer drugs, which are often co-administered at maximum tolerated doses with other drugs as part of a palliative treatment regimen.