Soham Mukherjee, Himanshu Bhagbole, Suraj Kumar, Krishna Kumar
PERSONALISED DOSING OF MEDICINES FOR CHILDREN *
Аннотация:
Doses for most drugs are determined from population‐level information, resulting in a standard ? one‐ size‐fits‐all’ dose range for all individuals. This review explores how doses can be personalized through the use of the individuals’ pharmacokinetic (PK)‐pharmacodynamic (PD) profile, its particular application in children, and therapy areas where such approaches have made inroads
Ключевые слова:
personalised dosing, medicine, health care
Key findings The Bayesian forecasting approach, based on population PK/PD models that account for variability in exposure and response, is a potent method for personalising drug therapy. Its potential utility is even greater in young children where additional sources of variability are observed such as maturation of eliminating enzymes and organs. The benefits of personalised dosing are most easily demonstrated for drugs with narrow therapeutic ranges such as antibiotics and cytotoxics and limited studies have shown improved outcomes. However, for a variety of reasons the approach has struggled to make more widespread impact at the bedside: complex dosing algorithms, high level of technical skills required, lack of randomised controlled clinical trials and the need for regulatory approval. Summary Personalised dosing will be a necessary corollary of the new precision medicine initiative. However, it faces a number of challenges that need to be overcome before such an approach to dosing in children becomes the norm. Introduction For the majority of drugs, therapeutic doses are proposed based on population‐level information, focussing on the typical patient and recommending a standard,‘one‐size‐ fits‐all’ fixed dose range. However, this approach to dosing does not to a great extent account for the prevalence of between‐patient variability in systemic exposure (pharmacokinetics (PK)) and the consequential biological response (pharmacodynamics (PD)). Demographic, genetic, clinical and environmental factors have been shown to contribute considerably to this population variability, and hence, individual patients can differ substantially in their response to drug therapy or their susceptibility to adverse drug reactions. This is of particular importance for drugs with narrow therapeutic ranges where the variability increases the likelihood of serious toxicity or otherwise treatment failure. Drug dosing in children has traditionally been extrapolated linearly from adult doses with adjustments based on age, body weight or body surface area. This method is easy, simple and does not require the use of complex dosing algorithms. However, the relationship between dose and age is not linear as children are in a continuous state of development and maturation and which can have a significant impact on both the PK and the PD of many drugs. Age‐related changes in absorption, distribution, metabolism and excretion and response to drugs have been demonstrated in young children as a result of the ontogeny process. Absorption, and hence the bioavailability, of orally administered drugs usually approach adult values by approximately 5 years of age because of alterations in gastric pH and gastrointestinal motility as well as the maturation process for efflux transporters and intestinal metabolism. The distribution of drugs is also affected in neonates and infants because of the increased total body water‐to‐body fat ratio and the decreased amount and affinity of plasma proteins, albumin and α1‐acid glycoprotein. In addition, hepatic metabolism and renal excretion of drugs are decreased in the first year of life, whereas the enzymatic activity of specific hepatic cytochrome P450 isoenzymes exceeds the adult values in the age range 1–12 years. The development process can also affect drug efficacy response or sensitivity to adverse effects. For instance, it has been shown that children are more sensitive to the anticoagulant effects of warfarin as compared to adults due to significantly lower levels of prothrombin and vitamin K‐dependant clotting factors. Therefore, a simple linear extrapolation of drug doses from adults to children may result in systemic exposures and/or clinical responses that are not equivalent in the two populations. Moreover, in terms of the challenges to optimising drug therapy, developmental effects should be considered an additional source of variability, increasing the complexity of drug treatment in children, particularly the neonatal and infant population. The risk‐benefit profile for all drugs is intrinsically linked to the doses administered and even more so for potent drugs with a narrow therapeutic range. From this perspective, it could be argued that where doses can be personalised for patients through an understanding of a drug's PK and PD response, and the factors contributing to their between‐ and within‐patient variability, the potential for improving the risk benefit profile is likely to be even greater in children. Personalising dosing in children through Bayesian forecasting Personalised dosing is a concept that recognises each individual has unique PK and PD characteristics, governing the time course of drug effect, and pivotal to optimising therapy. Knowledge of the individual's PK/PD parameters is therefore key to individualising drug doses and improving treatment response. The concept of Bayesian forecasting, a proactive approach to dose individualisation of drugs with narrow therapeutic ranges, was first introduced by Sheiner et al. in 1979 and was first applied on a microcomputer by Peck et al. The method utilises population PK/PD models, incorporating clinically significant covariates that explain the between‐ and within‐ patient variability, to prospectively identify individual's PK/PD parameters and hence personalise dosing. The Bayesian approach has been shown to be a major advance on traditional therapeutic drug monitoring (TDM), which is a reactive attempt at dosage individualisation. Bayesian forecasting has several advantages over traditional TDM; it can be used during complicated drug dosage regimens, at non‐steady state conditions, and when only a limited number (1 or 2) of serum drug data are available. The latter is of particular importance and a major advantage in the paediatric population where decreasing the aggressiveness of interventions is always preferable. To illustrate the difference between the two approaches, the example of aminoglycoside antibiotics will be discussed(Figure 1). The traditional method of monitoring aminoglycosides to ensure the target therapeutic range is achieved is to give an initial standard dose and then to measure the peak and trough concentrations at steady state. The observed concentrations are then used to adjust the dose, through the use of a nomogram or individualisation by estimating PK parameters, assuming a one‐ compartment model with clearance and volume of distribution and linear kinetics. However, often more complex models than one‐compartment may be required to avoid bias in parameters and the steady state condition is not always attainable, particularly in premature neonates and critically ill children with variable renal function that can alter the drug disposition. In contrast, in the Bayesian approach population PK models are implemented that incorporate not only the typical PK parameters describing aminoglycoside disposition but also the between‐ and within‐patient variability, and the covariates that explain this variability. An initial a priori estimation of the individual patient's PK parameters, and hence, dose can be predicted with greater certainty using the population parameters of the PK model (knowing that the model is representative of other patients in the population) and the individual patient covariates (age, weight, renal function etc).
Номер журнала Вестник науки №7 (16) том 1
Ссылка для цитирования:
Soham Mukherjee, Himanshu Bhagbole, Suraj Kumar, Krishna Kumar PERSONALISED DOSING OF MEDICINES FOR CHILDREN // Вестник науки №7 (16) том 1. С. 120 - 134. 2019 г. ISSN 2712-8849 // Электронный ресурс: https://www.вестник-науки.рф/article/1932 (дата обращения: 26.04.2024 г.)
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