[Pharmacokinetic changes related to acute infection. Examples from the SARS-CoV-2 pandemic]

COVID-19 infection and risk of adverse drug reactions: Cohort study

AIM

During coronavirus disease 2019 (COVID-19), the incidence rate of adverse drug reactions (ADRs) in hospitalized patients seemed higher than before the pandemic. Severe inflammation triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was cited as an explanation. We aimed to determine whether COVID-19 infection was associated with a higher risk of ADRs compared to other infectious diseases.

METHODS

A monocentric historic cohort, "exposed/unexposed" study, was conducted in the university hospital of Saint-Étienne (inclusion period from March 05, 2020 to April 16, 2020 for "COVID-19" and from January to December 2019 for "non-COVID-19"). All ADRs reported in patients' medical records were retrospectively assessed using Bégaud et al.'s algorithm. A multivariable Cox regression was performed to assess the hazard ratio (HR).

RESULTS

The incidence rate of 4.64 ADRs per person-month in the "COVID-19" group did not differ from the 3.52 ADRs per person-month in the "non-COVID-19" group (multivariable adjusted HR 1.29, 95% confidence interval [CI], 0.91-1.81, P=0.1436). COVID-19 patients had more hepatobiliary disorders whereas non-COVID-19 patients had more renal and urinary disorders. Classes of drugs mostly involved in ADRs occurrence were antibiotics, followed by antithrombotics in both groups. Compared to patients with no ADR, patients with ADRs had higher C-reactive protein (CRP) levels and a lower estimated glomerular filtration rate (eGFR).

CONCLUSION

In this study, the incidence rate in hospitalized patients with COVID-19 was not statistically different from that in the group with another infection. High CRP levels, as well as low eGFR, were the main risk factors for the occurrence of ADRs and should be considered in further ADR prevention strategies.

Towards precision dosing in psychiatry: Population pharmacokinetics meta-modelling of clozapine and lithium

BACKGROUND

Treatment optimization is mandatory in psychiatric diseases and the use of population pharmacokinetics (popPK) models through model informed precision dosing (MIPD) has the potential to improve patient medical care. In this perspective, meta-modelling methods could provide popPK models with improved predictive performances and most of covariates of interest. The aims of this study were to develop meta-models of clozapine and lithium, assess their predictability and propose optimized dosing regimens for both drugs.

METHODS

Two popPK models for each drug were retained to develop the meta-models. For clozapine, the model with the best predictive performances and gender as a covariate and one with smoking status were retained. For lithium, the model with the best predictive performances and fat-free mass as covariate and one with glomerular filtration rate were retained.

RESULTS

Both meta-models showed improved predictability compared to the original models. Clozapine meta-model simulations allowed us to propose dosing regimen according to gender and smoking status. Steady-state doses ranged from 375 to 725 mg/day for clozapine once daily, and from 350 to 650 mg/day for clozapine twice daily. Lithium meta-model simulations allowed us to propose dosing regimen according to weight, body mass index, gender and GFR. Our steady-state dose propositions ranged from 625 to 1125 mg/day for males, and from 375 to 750 mg/day for females.

CONCLUSION

Both meta-models met the acceptability criteria for use in clinical practice on all subpopulations of interest. Those models could be used in the perspective of MIPD for clozapine and lithium.

Towards Precision Dosing of Clozapine in Schizophrenia: External Evaluation of Population Pharmacokinetic Models and Bayesian Forecasting

BACKGROUND

Therapeutic drug monitoring and treatment optimization of clozapine are recommended, owing to its narrow therapeutic range and pharmacokinetic (PK) variability. This study aims to assess the clinical applicability of published population PK models by testing their predictive performance in an external data set and to determine the effectiveness of Bayesian forecasting (BF) for clozapine treatment optimization.

METHODS

Available models of clozapine were identified, and their predictive performance was determined using an external data set (53 patients, 151 samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated. The final concentration was reestimated for all patients using covariates or previously observed concentrations.

RESULTS

The 7 included models presented limited predictive performance. Only 1 model met the acceptability criteria (median PE ≤ ±20% and median absolute PE ≤30%). There was no difference between the data used for building the models (therapeutic drug monitoring or PK study) or the number of compartments in the models. A tendency for higher inaccuracy at low concentrations during treatment initiation was observed. Heterogeneities were observed in the predictive performances between the subpopulations, especially in terms of smoking status and sex. For the models included, BF significantly improved their predictive performance.

CONCLUSIONS

Our study showed that upon external evaluation, clozapine models provide limited predictive performance, especially in subpopulations such as nonsmokers. From the perspective of model-informed prediction dosing, model predictability should be improved using updating or metamodeling methods. Moreover, BF substantially improved model predictability and could be used for clozapine treatment optimization.