Comorbidities and the right dose: antipsychotics.

INTRODUCTION: The effects of antipsychotic drugs are dose-dependent, which is particularly true for their efficacy, each antipsychotic having a specific dose-response curve. This may justify individualizing doses for these agents. AREAS COVERED: We review the pharmacokinetic profiles of seven oral antipsychotics: haloperidol, risperidone, olanzapine, clozapine, quetiapine, ziprasidone, and aripiprazole. Their main indications are psychotic and affective disorders. They are prescribed in a very large population which may have comorbidities. Hence, we analyze the impact of the latter on the pharmacokinetic profiles of these antipsychotics, focusing on renal and hepatic impairment. Reviews and clinical trials were discussed based on a systematic literature search (PubMed) ranging from 1995 to 2022. EXPERT OPINION: Factors liable to impact antipsychotic dosage are numerous and their subsequent effects often hard to predict, due to multilevel interactions and compensatory phenomena. In clinical practice, physicians must be aware of these potential effects, but base their decisions on monitoring antipsychotic plasma levels.

Free and Open-Source Posologyr Software for Bayesian Dose Individualization: An Extensive Validation on Simulated Data.

Model-informed precision dosing is being increasingly used to improve therapeutic drug monitoring. To meet this need, several tools have been developed, but open-source software remains uncommon. Posologyr is a free and open-source R package developed to enable Bayesian individual parameter estimation and dose individualization. Before using it for clinical practice, performance validation is mandatory. The estimation functions implemented in posologyr were benchmarked against reference software products on a wide variety of models and pharmacokinetic profiles: 35 population pharmacokinetic models, with 4.000 simulated subjects by model. Maximum A Posteriori (MAP) estimates were compared to NONMEM post hoc estimates, and full posterior distributions were compared to Monolix conditional distribution estimates. The performance of MAP estimation was excellent in 98.7% of the cases. Considering the full posterior distributions of individual parameters, the bias on dosage adjustment proposals was acceptable in 97% of cases with a median bias of 0.65%. These results confirmed the ability of posologyr to serve as a basis for the development of future Bayesian dose individualization tools.

Personalization of drug dose thematic issue: introduction article.

INTRODUCTION: The need to individualize a drug dosage and adjust it to a patient's physiological and/or pathophysiological status is rarely satisfied in routine clinical practice, primarily because of complexity of the adjustment task. AREAS COVERED: The aim of this article is to shed light to basic principles of drug dosage individualization in the most frequent clinical states that affect pharmacokinetics of drugs. The principles are derived from published clinical studies conducted on diverse patient populations, using non-compartmental pharmacokinetic model. EXPERT OPINION: Simple, but sufficiently exact, way to calculate appropriate drug dose for a patient is the one based on target average steady-state concentration and non-compartmental pharmacokinetic model. If target steady-state drug concentration and dosage interval are considered fixed, maintenance dose could be adjusted on the basis of expected changes of total drug clearance and bioavailability, while loading dose should be related to changes of volume of distribution and bioavailability. Relative increase or decrease of these pharmacokinetic parameters in regard to normal values in healthy persons is translated to relative (percentual) increase or decrease of maintenance and loading doses recommended in the drug monograph.

The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib.

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

Individualization of FSH Doses in Assisted Reproduction: Facts and Fiction.

The art of ovarian stimulation for IVF/ICSI treatment using exogenous FSH should be balanced against the relative contribution of other steps of the ART process such as the IVF-lab-phase and the Embryo-Transfer. The aim of ovarian stimulation is to obtain a certain number of oocytes, that will enable the best probability of achieving a live birth. It has been suggested that more oocytes will create a better prospect for pregnancy, but studies on the question whether the retrieval of a few oocytes less or more will make the difference are not clearly supportive for this mantra. Personalization strategies have been the subject of many studies over the past 20 years. Creating the optimal response in a patient in terms of live birth prognosis as well as OHSS risks may be based on information from the Ovarian Reserve testing using the Antral Follicle Count or Anti-Mullerian Hormone, the patient's bodyweight, the ovarian response in a previous cycle, and the dosage level of FSH. Taken together, steering the ovarian response into a supposed optimal range may appear difficult as the interrelation for each of these factors with the egg number is weak. Using OR testing for choosing FSH dosage, compared to a standard normal dosage of 150 IU, has been studied in several trials. Dosage individualization, in general, does not appear to improve the prospects for live birth, but the reduction in OHSS risk may be substantial. This implies that the use of high dosages of FSH in predicted LOW responders lacks any cost-benefit for the patient and may be abandoned, while in predicted HIGH responders, reduction of the usual dosage level of 150 IU may create better safety, provided that in case of an unexpected LOW response cancelation of the cycle is refrained from. In view of recent developments in using GnRH agonist triggering of final oocyte maturation, the trend could be that with the Antagonist co-medication system and a standard dosage of 150 IU of FSH, prior ovarian reserve testing may become futile, as safety can be managed well in actual HIGH responders by replacing the high dose hCG trigger.

Role of Statistical Random-Effects Linear Models in Personalized Medicine.

Some empirical studies and recent developments in pharmacokinetic theory suggest that statistical random-effects linear models are valuable tools that allow describing simultaneously patient populations as a whole and patients as individuals. This remarkable characteristic indicates that these models may be useful in the development of personalized medicine, which aims at finding treatment regimes that are appropriate for particular patients, not just appropriate for the average patient. In fact, published developments show that random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. In particular, individualized dosages computed with these models by means of an empirical Bayesian approach may produce better results than dosages computed with some methods routinely used in therapeutic drug monitoring. This is further supported by published empirical and theoretical findings that show that random effects linear models may provide accurate representations of phase III and IV steady-state pharmacokinetic data, and may be useful for dosage computations. These models have applications in the design of clinical algorithms for drug dosage individualization in chronic diseases; in the computation of dose correction factors; computation of the minimum number of blood samples from a patient that are necessary for calculating an optimal individualized drug dosage in therapeutic drug monitoring; measure of the clinical importance of clinical, demographic, environmental or genetic covariates; study of drug-drug interactions in clinical settings; the implementation of computational tools for web-site-based evidence farming; design of pharmacogenomic studies; and in the development of a pharmacological theory of dosage individualization.