Dataset Information

Alternative dosing regimens for atezolizumab: an example of model-informed drug development in the postmarketing setting.

ABSTRACT: PURPOSE:To determine the exposure-response (ER) relationships between atezolizumab exposure and efficacy or safety in patients with advanced non-small cell lung cancer (NSCLC) or urothelial carcinoma (UC) and to identify alternative dosing regimens. METHODS:ER analyses were conducted using pooled NSCLC and UC data from phase 1 and 3 studies (PCD4989g, OAK, IMvigor211; ClinicalTrials.gov IDs, NCT01375842, NCT02008227, and NCT02302807, respectively). Objective response rate, overall survival, and adverse events were evaluated vs pharmacokinetic (PK) metrics. Population PK-simulated exposures for regimens of 840 mg every 2 weeks (q2w) and 1680 mg every 4 weeks (q4w) were compared with the approved regimen of 1200 mg every 3 weeks (q3w) and the maximum assessed dose (MAD; 20 mg/kg q3w). Phase 3 IMpassion130 (NCT02425891) data were used to validate the PK simulations for 840 mg q2w. Observed safety data were evaluated by exposure and body weight subgroups. RESULTS:No significant ER relationships were observed for safety or efficacy. Predicted exposures for 840 mg q2w and 1680 mg q4w were comparable to 1200 mg q3w and the MAD and consistent with observed PK data from IMpassion130. Observed safety was similar between patients with a Cmax above and below the predicted Cmax for 1680 mg q4w and between patients in the lowest and upper 3 body weight quartiles. CONCLUSION:Atezolizumab regimens of 840 mg q2w and 1680 mg q4w are expected to have comparable efficacy and safety as the approved regimen of 1200 mg q3w, supporting their interchangeable use and offering patients greater flexibility.

SUBMITTER: Morrissey KM

PROVIDER: S-EPMC6820606 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Alternative dosing regimens for atezolizumab: an example of model-informed drug development in the postmarketing setting.

Morrissey Kari M KM Marchand Mathilde M Patel Hina H Zhang Rong R Wu Benjamin B Phyllis Chan H H Mecke Almut A Girish Sandhya S Jin Jin Y JY Winter Helen R HR Bruno René R

Cancer chemotherapy and pharmacology 20190921 6

<h4>Purpose</h4>To determine the exposure-response (ER) relationships between atezolizumab exposure and efficacy or safety in patients with advanced non-small cell lung cancer (NSCLC) or urothelial carcinoma (UC) and to identify alternative dosing regimens.<h4>Methods</h4>ER analyses were conducted using pooled NSCLC and UC data from phase 1 and 3 studies (PCD4989g, OAK, IMvigor211; ClinicalTrials.gov IDs, NCT01375842, NCT02008227, and NCT02302807, respectively). Objective response rate, overall ...[more]

PMID: 31542806

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Project description:Rifampicin exhibits complexities in its pharmacokinetics (PK), including high inter-occasion variability (IOV), which is challenging for dose individualization. Model-informed precision dosing (MIPD) can be used to optimize individual doses. In this simulation-based study we investigated the magnitude of IOV in rifampicin PK on an exposure level, the impact of not acknowledging IOV when performing MIPD, and the number of sampling occasions needed to forecast the dose. Subjects with drug-susceptible tuberculosis (TB) were simulated from a previously developed population PK model. To explore the magnitude of IOV, the area under the plasma concentration-time curve from time zero up to 24 h (AUC0-24h) after 35 mg/kg in the typical individual was simulated for 1,000 sampling occasions at steady-state. The impact of ignoring IOV for dose predictions was investigated by comparing the prediction error of a MIPD approach including IOV to an approach ignoring IOV. Furthermore, the number of sampling occasions needed to predict individual doses using a MIPD approach was assessed. The AUC0-24h in the typical individual varied substantially between simulated sampling occasions [95% prediction interval (PI): 122.2 to 331.2 h mg/L], equivalent to an IOV in AUC0-24h of 25.8%, compared to an inter-individual variability of 25.4%. The median of the individual prediction errors using a MIPD approach incorporating IOV was 0% (75% PI: -14.6% to 0.0%), and the PI for the individual prediction errors was narrower with than without IOV (median: 0%, 75% PI: -14.6% to 20.0%). The most common target dose in this population was forecasted correctly in 95% of the subjects when IOV was included in MIPD. In subjects where doses were not predicted optimally, a lower dose was predicted compared to the target, which is favorable from a safety perspective. Moreover, the imprecision (relative root mean square error) and bias in predicted doses using MIPD with IOV decreased statistically significant when a second sampling occasion was added (difference in imprecision: -9.1%, bias: -7.7%), but only marginally including a third (difference in imprecision: -0.1%, bias: -0.1%). In conclusion, a large variability in exposure of rifampicin between occasions was shown. In order to forecast the individual dose correctly, IOV must be acknowledged which can be achieved using a MIPD approach with PK information from at least two sampling occasions.

Project description:Dolutegravir-based regimens are recommended as first-line therapy for HIV in low- and middle-income countries where tuberculosis is the most common opportunistic infection. Concurrent HIV/tuberculosis treatment is challenging because of drug-drug interactions. Our analysis aimed to characterize dolutegravir's population pharmacokinetics when coadministered with rifampicin and assess alternative dolutegravir dosing regimens. We developed a population pharmacokinetic model of dolutegravir in NONMEM with data from two healthy-volunteer studies (RADIO and ClinicalTrials.gov identifier NCT01231542) and validated it with data from the INSPIRING study, which consisted of participants living with HIV. The model was developed with 817 dolutegravir plasma concentrations from 41 participants. A 2-compartment model with first-order elimination and lagged absorption best described dolutegravir's pharmacokinetics. For a typical 70-kg individual, we estimated a clearance, absorption rate constant, central volume, and peripheral volume of 1.03 L/h, 1.61 h-1, 12.7 L, and 3.85 L, respectively. Rifampicin coadministration increased dolutegravir clearance by 144% (95% confidence interval [CI], 126 to 161%). Simulations showed that when 50 or 100 mg once-daily dolutegravir is coadministered with rifampicin in 70-kg individuals, 71.7% and 91.5% attain trough concentrations above 0.064 mg/L, the protein-adjusted 90% inhibitory concentration (PA-IC90), respectively. The model developed from healthy-volunteer data describes patient data reasonably well but underpredicts trough concentrations. Although 50 mg of dolutegravir given twice daily achieves target concentrations in more than 99% of individuals cotreated with rifampicin, 100 mg of dolutegravir, once daily, in the same population is predicted to achieve satisfactory pharmacokinetic target attainment. The efficacy of this regimen should be investigated since it presents an opportunity for treatment simplification.

Project description:Introduction: Ceftriaxone is one of commonly prescribed beta-lactam antibiotics with several label and off-label clinical indications. A high fraction of administered dose of ceftriaxone is excreted renally in an unchanged form, and it may accumulate significantly in patients with impaired renal functions, which may lead to toxicity. Methods: In this study, we employed a physiologically-based pharmacokinetic (PBPK) modeling, as a tool for precision dosing, to predict the biological exposure of ceftriaxone in a virtually-constructed healthy and chronic kidney disease patient populations, with subsequent dosing optimizations. We started developing the model by integrating the physicochemical properties of the drug with biological system information in a PBPK software platform. A PBPK model in an adult healthy population was developed and evaluated visually and numerically with respect to experimental pharmacokinetic data. The model performance was evaluated based on the fold error criteria of the predicted and reported values for different pharmacokinetic parameters. Then, the model was applied to predict drug exposure in CKD patient populations with various degrees of severity. Results: The developed PBPK model was able to precisely describe the pharmacokinetic behavior of ceftriaxone in adult healthy population and in mild, moderate, and severe CKD patient populations. Decreasing the dose by approximately 25% in mild and 50% in moderate to severe renal disease provided a comparable exposure to the healthy population. Based on the simulation of multiple dosing regimens in severe CKD population, it has been found that accumulation of 2 g every 24 h is lower than the accumulation of 1 g every 12 h dosing regimen. Discussion: In this study, the observed concentration time profiles and pharmacokinetic parameters for ceftriaxone were successfully reproduced by the developed PBPK model and it has been shown that PBPK modeling can be used as a tool for precision dosing to suggest treatment regimens in population with renal impairment.

Project description:BackgroundNovel drug regimens are needed for tuberculosis (TB) treatment. New regimens aim to improve on characteristics such as duration, efficacy, and safety profile, but no single regimen is likely to be ideal in all respects. By linking these regimen characteristics to a novel regimen's ability to reduce TB incidence and mortality, we sought to prioritize regimen characteristics from a population-level perspective.Methods and findingsWe developed a dynamic transmission model of multi-strain TB epidemics in hypothetical populations reflective of the epidemiological situations in India (primary analysis), South Africa, the Philippines, and Brazil. We modeled the introduction of various novel rifampicin-susceptible (RS) or rifampicin-resistant (RR) TB regimens that differed on six characteristics, identified in consultation with a team of global experts: (1) efficacy, (2) duration, (3) ease of adherence, (4) medical contraindications, (5) barrier to resistance, and (6) baseline prevalence of resistance to the novel regimen. We compared scale-up of these regimens to a baseline reflective of continued standard of care. For our primary analysis situated in India, our model generated baseline TB incidence and mortality of 157 (95% uncertainty range [UR]: 113-187) and 16 (95% UR: 9-23) per 100,000 per year at the time of novel regimen introduction and RR TB incidence and mortality of 6 (95% UR: 4-10) and 0.6 (95% UR: 0.3-1.1) per 100,000 per year. An optimal RS TB regimen was projected to reduce 10-y TB incidence and mortality in the India-like scenario by 12% (95% UR: 6%-20%) and 11% (95% UR: 6%-20%), respectively, compared to current-care projections. An optimal RR TB regimen reduced RR TB incidence by an estimated 32% (95% UR: 18%-46%) and RR TB mortality by 30% (95% UR: 18%-44%). Efficacy was the greatest determinant of impact; compared to a novel regimen meeting all minimal targets only, increasing RS TB treatment efficacy from 94% to 99% reduced TB mortality by 6% (95% UR: 1%-13%, half the impact of a fully optimized regimen), and increasing the efficacy against RR TB from 76% to 94% lowered RR TB mortality by 13% (95% UR: 6%-23%). Reducing treatment duration or improving ease of adherence had smaller but still substantial impact: shortening RS TB treatment duration from 6 to 2 mo lowered TB mortality by 3% (95% UR: 1%-6%), and shortening RR TB treatment from 20 to 6 mo reduced RR TB mortality by 8% (95% UR: 4%-13%), while reducing nonadherence to the corresponding regimens by 50% reduced TB and RR TB mortality by 2% (95% UR: 1%-4%) and 6% (95% UR: 3%-10%), respectively. Limitations include sparse data on key model parameters and necessary simplifications to model structure and outcomes.ConclusionsIn designing clinical trials of novel TB regimens, investigators should consider that even small changes in treatment efficacy may have considerable impact on TB-related incidence and mortality. Other regimen improvements may still have important benefits for resource allocation and outcomes such as patient quality of life.

Dataset Information

Alternative dosing regimens for atezolizumab: an example of model-informed drug development in the postmarketing setting.

Publications

Alternative dosing regimens for atezolizumab: an example of model-informed drug development in the postmarketing setting.

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