Supporting Precision Dosing in Drug Labeling

The drug titration paradox: a control engineering perspective

PURPOSE OF REVIEW

The drug titration paradox describes that, from a population standpoint, drug doses appear to have a negative correlation with its clinical effect. This paradox is a relatively modern discovery in anesthetic pharmacology derived from large clinical data sets. This review will interpret the paradox using a control engineering perspective.

RECENT FINDINGS

Drug titration is a challenging endeavor, and the medication delivery systems used in everyday clinical practice, including infusion pumps and vaporizers, typically do not allow for rapid or robust titration of medication being delivered. In addition, clinicians may be reluctant to deviate from a predetermined plan or may be content to manage patients within fixed goal boundaries.

SUMMARY

This drug titration paradox describes the constraints of how the average clinician will dose a patient with an unknown clinical response. While our understanding of the paradox is still in its infancy, it remains unclear how alternative dosing schemes, such as through automation, may exceed the boundaries of the paradox and potentially affect its conclusions.

Effect of Obesity on the Pharmacokinetics and Pharmacodynamics of Anticancer Agents

An objective of the Precision Medicine Initiative, launched in 2015 by the US Food and Drug Administration and National Institutes of Health, is to optimize and individualize dosing of drugs, especially anticancer agents, with high pharmacokinetic and pharmacodynamic variability. The American Society of Clinical Oncology recently reported that 40% of obese patients receive insufficient chemotherapy doses and exposures, which may lead to reduced efficacy, and recommended pharmacokinetic studies to guide appropriate dosing in these patients. These issues will only increase in importance as the incidence of obesity in the population increases. This publication reviews the effects of obesity on (1) tumor biology, development of cancer, and antitumor response; (2) pharmacokinetics and pharmacodynamics of small-molecule anticancer drugs; and (3) pharmacokinetics and pharmacodynamics of complex anticancer drugs, such as carrier-mediated agents and biologics. These topics are not only important from a scientific research perspective but also from a drug development and regulator perspective. Thus, it is important to evaluate the effects of obesity on the pharmacokinetics and pharmacodynamics of anticancer agents in all categories of body habitus and especially in patients who are obese and morbidly obese. As the effects of obesity on the pharmacokinetics and pharmacodynamics of anticancer agents may be highly variable across drug types, the optimal dosing metric and algorithm for difference classes of drugs may be widely different. Thus, studies are needed to evaluate current and novel metrics and methods for measuring body habitus as related to optimizing the dose and reducing pharmacokinetic and pharmacodynamic variability of anticancer agents in patients who are obese and morbidly obese.

Dose/exposure-response modeling in dose titration trials: Overcoming the titration paradox

Response-based dose individualization or dose titration is a powerful approach to achieve precision dosing. Yet, titration as an individualization strategy is underused in drug development and therefore not reflected in labeling, possibly partly because of the data analysis challenges associated with assessing dose/exposure-response under dose titration, where there is an inherent risk of selection bias because poor responders would get high doses, whereas good responders would get low doses. In a recent article, this issue of selection bias was termed the "titration paradox." In this study, we demonstrate by means of simulation that the titration paradox may be overcome if longitudinal data from dose titration trials is analyzed using a population approach that accounts for the fact that dose/exposure-response relationships differ between individuals. We show that with an appropriate sample size and missing data missing at random, stepwise dose/exposure-response modeling based on data obtained under dose titration is not by definition subject to model selection bias or bias in parameter estimates. We also illustrate the challenges of graphical exploration of data obtained under dose titration and discuss the use of model diagnostic tools with such data. Our study shows that if, at every timepoint in the course of a trial, there is a clear causal relationship between the response and the dose/exposure level, and a population approach is used, it will in many cases be possible to develop, estimate, and appropriately qualify a dose/exposure-response model also for data obtained under dose titration, thus overcoming the titration paradox.

Model enhanced reinforcement learning to enable precision dosing: A theoretical case study with dosing of propofol

Extending the potential of precision dosing requires evaluating methodologies offering more flexibility and higher degree of personalization. Reinforcement learning (RL) holds promise in its ability to integrate multidimensional data in an adaptive process built toward efficient decision making centered on sustainable value creation. For general anesthesia in intensive care units, RL is applied and automatically adjusts dosing through monitoring of patient's consciousness. We further explore the problem of optimal control of anesthesia with propofol by combining RL with state-of-the-art tools used to inform dosing in drug development. In particular, we used pharmacokinetic-pharmacodynamic (PK-PD) modeling as a simulation engine to generate experience from dosing scenarios, which cannot be tested experimentally. Through simulations, we show that, when learning from retrospective trial data, more than 100 patients are needed to reach an accuracy within the range of what is achieved with a standard dosing solution. However, embedding a model of drug effect within the RL algorithm improves accuracy by reducing errors to target by 90% through learning to take dosing actions maximizing long-term benefit. Data residual variability impacts accuracy while the algorithm efficiently coped with up to 50% interindividual variability in the PK and 25% in the PD model's parameters. We illustrate how extending the state definition of the RL agent with meaningful variables is key to achieve high accuracy of optimal dosing policy. These results suggest that RL constitutes an attractive approach for precision dosing when rich data are available or when complemented with synthetic data from model-based tools used in model-informed drug development.

Key Challenges in Providing Effective Antibiotic Therapy for Critically Ill Patients with Bacterial Sepsis and Septic Shock

Early initiation of effective antibiotic therapy is vitally important for saving the lives of critically ill patients with sepsis or septic shock. The susceptibility of the infecting pathogen and the ability of the selected dosage regimen to safely achieve the required antibiotic exposure need to be carefully considered to achieve a high probability of a successful outcome. Critically ill patients commonly experience substantial pathophysiological changes that impact the functions of various organs, including the kidneys. Many antibiotics are predominantly renally eliminated and thus renal function is a major determinant of the regimen needed to achieve the required antibiotic exposure. However, currently, there is a paucity of guidelines to inform antibiotic dosing in critically ill patients, including those with sepsis or septic shock. This paper briefly reviews methods that are commonly used in critically ill patients to provide a measure of renal function, and approaches that describe the relationship between the exposure to an antibiotic and its antibacterial effects. Two common conditions that very substantially complicate the use of antibiotics in critically ill patients with sepsis, unstable renal function, and augmented renal clearance, are considered in detail and their potential therapeutic implications are explored. Suggestions are provided on how treatment of bacterial infections in critically ill patients with sepsis might be improved. Of high potential are model-informed approaches that aim to individualize initial treatment regimens based on patient and bacterial characteristics, with refinement of regimens during treatment in response to monitoring antibiotic concentrations, responsive measures of renal function, and other important clinical data.