Tony K. L. Kiang

Fundamentals of Population Pharmacokinetic Modelling

Population pharmacokinetic modelling is widely used within the field of clinical pharmacology as it helps to define the sources and correlates of pharmacokinetic variability in target patient populations and their impact upon drug disposition; and population pharmacokinetic modelling provides an estimation of drug pharmacokinetic parameters. This method’s defined outcome aims to understand how participants in population pharmacokinetic studies are representative of the population as opposed to the healthy volunteers or highly selected patients in traditional pharmacokinetic studies. This review focuses on the fundamentals of population pharmacokinetic modelling and how the results are evaluated and validated.This review defines the common aspects of population pharmacokinetic modelling through a discussion of the literature describing the techniques and placing them in the appropriate context. The concept of validation, as applied to population pharmacokinetic models, is explored focusing on the lack of consensus regarding both terminology and the concept of validation itself.Population pharmacokinetic modelling is a powerful approach where pharmacokinetic variability can be identified in a target patient population receiving a pharmacological agent. Given the lack of consensus on the best approaches in model building and validation, sound fundamentals are required to ensure the selected methodology is suitable for the particular data type and/or patient population. There is a need to further standardize and establish the best approaches in modelling so that any model created can be systematically evaluated and the results relied upon.

Fundamentals of population pharmacokinetic modelling: modelling and software.

Population pharmacokinetic modelling is widely used within the field of clinical pharmacology as it helps to define the sources and correlates of pharmacokinetic variability in target patient populations and their impact upon drug disposition. This review focuses on the fundamentals of population pharmacokinetic modelling and provides an overview of the commonly available software programs that perform these functions. This review attempts to define the common, fundamental aspects of population pharmacokinetic modelling through a discussion of the literature describing the techniques and placing them in the appropriate context. An overview of the most commonly available software programs is also provided. Population pharmacokinetic modelling is a powerful approach where sources and correlates of pharmacokinetic variability can be identified in a target patient population receiving a pharmacological agent. There is a need to further standardize and establish the best approaches in modelling so that any model created can be systematically evaluated and the results relied upon. Various nonlinear mixed-effects modelling methods, packaged in a variety of software programs, are available today. When selecting population pharmacokinetic software programs, the consumer needs to consider several factors, including usability (e.g. user interface, native platform, price, input and output specificity, as well as intuitiveness), content (e.g. algorithms and data output) and support (e.g. technical and clinical).

Fundamentals of population pharmacokinetic modelling: validation methods.

Population pharmacokinetic modelling is widely used within the field of clinical pharmacology as it helps to define the sources and correlates of pharmacokinetic variability in target patient populations and their impact upon drug disposition; and population pharmacokinetic modelling provides an estimation of drug pharmacokinetic parameters. This methods defined outcome aims to understand how participants in population pharmacokinetic studies are representative of the population as opposed to the healthy volunteers or highly selected patients in traditional pharmacokinetic studies. This review focuses on the fundamentals of population pharmacokinetic modelling and how the results are evaluated and validated. This review defines the common aspects of population pharmacokinetic modelling through a discussion of the literature describing the techniques and placing them in the appropriate context. The concept of validation, as applied to population pharmacokinetic models, is explored focusing on the lack of consensus regarding both terminology and the concept of validation itself. Population pharmacokinetic modelling is a powerful approach where pharmacokinetic variability can be identified in a target patient population receiving a pharmacological agent. Given the lack of consensus on the best approaches in model building and validation, sound fundamentals are required to ensure the selected methodology is suitable for the particular data type and/or patient population. There is a need to further standardize and establish the best approaches in modelling so that any model created can be systematically evaluated and the results relied upon.

Revolutionizing Therapeutic Drug Monitoring with the Use of Interstitial Fluid and Microneedles Technology

While therapeutic drug monitoring (TDM) that uses blood as the biological matrix is the traditional gold standard, this practice may be impossible, impractical, or unethical for some patient populations (e.g., elderly, pediatric, anemic) and those with fragile veins. In the context of finding an alternative biological matrix for TDM, this manuscript will provide a qualitative review on: (1) the principles of TDM; (2) alternative matrices for TDM; (3) current evidence supporting the use of interstitial fluid (ISF) for TDM in clinical models; (4) the use of microneedle technologies, which is potentially minimally invasive and pain-free, for the collection of ISF; and (5) future directions. The current state of knowledge on the use of ISF for TDM in humans is still limited. A thorough literature review indicates that only a few drug classes have been investigated (i.e., anti-infectives, anticonvulsants, and miscellaneous other agents). Studies have successfully demonstrated techniques for ISF extraction from the skin but have failed to demonstrate commercial feasibility of ISF extraction followed by analysis of its content outside the ISF-collecting microneedle device. In contrast, microneedle-integrated biosensors built to extract ISF and perform the biomolecule analysis on-device, with a key feature of not needing to transfer ISF to a separate instrument, have yielded promising results that need to be validated in pre-clinical and clinical studies. The most promising applications for microneedle-integrated biosensors is continuous monitoring of biomolecules from the skin’s ISF. Conducting TDM using ISF is at the stage where its clinical utility should be investigated. Based on the advancements described in the current review, the immediate future direction for this area of research is to establish the suitability of using ISF for TDM in human models for drugs that have been found suitable in pre-clinical experiments.

Conclusion and Clinical Decision Algorithm

We have conducted a systematic qualitative review on the pharmacokinetic and pharmacodynamic drug-drug interactions associated with antiretroviral drugs currently recommended by the World Health Organization. The limitations and future directions have been provided in each Chapter Summary. Here, we will present a Clinical Decision Algorithm (modified from a previous algorithm for antimalarials) [1] to help clinicians assess the relevance of identified drug-drug interactions associated with antiretroviral agents.

HIV-Associated Comorbidities

This chapter will review common noninfectious and infectious comorbidities that place patients at risk of polypharmacy and associated pharmacokinetic and pharmacodynamic drug interactions with antiretroviral agents.

Pharmacodynamic Interactions Between Antiretrovirals and Other Agents

This chapter will provide an overview of pharmacodynamic drug interactions associated with antiretroviral agents and commonly co-administered agents. By the end of this chapter, the reader will develop an understanding of pharmacodynamic interactions and how they may positively or negatively impact care.

Clinical Significant Interactions with Opioid Analgesics

This chapter summarizes the pharmacokinetic drug interactions of select opioid agents, focusing on underlying molecular mechanisms (e.g., known metabolic interactions at the enzymatic and transporter levels, such as cytochrome P450 [CYP450], uridine 5’-diphospho-glucuronosyltransferases [UGT], and drug transporters) and drawing a connection to pharmacodynamic interactions in clinical studies. The majority of data has focused on drug metabolism, and there are in vitro data to support the in vivo observations. Many opioids (e.g., codeine) are metabolized by enzymes that are known to exhibit genetic polymorphism, and this additional (gene-drug interaction) factor must be considered. Most data on opioids have focused on their classical analgesic properties, effects on pain threshold, and adverse effects such as somnolence, nausea/vomiting, gastrointestinal motility, or miosis. Additional atypical adverse effects such QTC prolongation (e.g., associated with methadone) or serotonin syndrome (e.g., associated with tramadol) must be considered and can be manifested by pharmacokinetic-associated pharmacodynamic interactions. Information on pharmacokinetic-mediated pharmacodynamic interactions is relatively scarce in the literature compared to the available pharmacokinetic data. The available human data for opioids only represent a small fraction of all the possible drug interactions but one may use various in vitro or in silico approaches to aid the prediction of pharmacokinetic interactions. Evidence that a significant pharmacokinetic interaction is associated with a pharmacodynamic interaction must be appropriately weighted based on limitations in the design of existing studies. This chapter concludes with a proposed clinical decision-making algorithm that may be used to ascertain the clinical significance of pharmacokinetic-mediated pharmacodynamic interactions with opioid analgesics:

In Vitro Reaction Phenotyping and Drug Interaction Data

This chapter summarizes the in vitro reaction phenotyping and drug interaction data for each antiretroviral agent. Investigations on the relative contributions of specific metabolizing enzymes and molecular enzyme inhibition/induction reactions will be presented for the following agents, based on drug class: n n nNonnucleoside reverse transcriptase inhibitors (NNRTIs): delavirdine, efavirenz, etravirine, nevirapine, and rilpivirine n n nNucleoside reverse-transcriptase inhibitors (NRTIs): abacavir, didanosine, emtricitabine, lamivudine, stavudine, tenofovir, and zidovudine n n nProtease inhibitors (PIs): atazanavir, darunavir, fosamprenavir, indinavir, nelfinavir, ritonavir, saquinavir, tipranavir, and lopinavir n n nFusion inhibitors: enfuvirtide n n nEntry inhibitors: maraviroc n n nIntegrase inhibitors: dolutegravir, elvitegravir, raltegravir

Clinical Drug-Drug Interaction Data: Effects of Co-administered Drugs on Pharmacokinetics of Antiretroviral Agents

This chapter summarizes the clinical drug-drug interaction data for each antiretroviral agent. The effects of co-administered drugs on the pharmacokinetics of the following agents will be presented: n n nNonnucleoside reverse transcriptase inhibitors (NNRTIs): delavirdine, efavirenz, etravirine, nevirapine, and rilpivirine n n nNucleoside reverse-transcriptase inhibitors (NRTIs): abacavir, didanosine, and zidovudine n n nProtease inhibitors (PIs): atazanavir, darunavir, fosamprenavir, emtricitabine, lamivudine, stavudine, tenofovir, indinavir, nelfinavir, ritonavir, saquinavir, tipranavir, and lopinavir n n nFusion Inhibitors: enfuvirtide n n nEntry inhibitors: maraviroc n n nIntegrase inhibitors: dolutegravir, elvitegravir, raltegravir