A Prediction Model of Drug Exposure in Cirrhotic Patients According to Child-Pugh Classification

Conventional cytotoxic chemotherapy for gastrointestinal cancer in patients with cirrhosis: A multicentre case-control study

BACKGROUND & AIMS

Progresses in management make a higher proportion of cirrhotic patients with gastrointestinal (GI) cancer candidates to chemotherapy. Data are needed on the safety and liver-related events associated with the use of chemotherapy in these patients.

METHODS

Forty-nine patients with cirrhosis receiving chemotherapy against GI cancer from 2013 to 2018 were identified in the French Health Insurance Database using ICD-10 codes K70-K74, and matched 1:2 to non-cirrhotic controls (n = 98) on age, tumour type and type of treatment. Adverse events (AE), dose tapering, discontinuation rate, liver-related events and survival rate were compared.

RESULTS

Patients with cirrhosis (Child-Pugh A 91%) more often received lower doses (38.8% vs 7.1%, p < .001), without significant differences in terms of grade 3/4 AE or dose tapering rates (29.6% vs. 36.7%; 22.3% vs 24.4%, respectively). Treatment discontinuation rate was higher in patients with cirrhosis (23.3% vs. 11.3%, p = .005). Child-Pugh (p = .007) and MELD (p = .025) scores increased under chemotherapy. Five patients with cirrhosis (10.2%) had liver decompensation within 12 months, and 17.2% of deaths in the cirrhosis group were liver-related versus 0% in matched controls. WHO-PS stage > 1 (HR 3.74, CI95%: 2.13-6.57, p < .001), TNM-stage M1 (HR 3.61, CI 95%: 1.82-7.16, p < .001), non-colorectal cancer (HR 1.73, CI 95%: 1.05-2.86, p = .032) and bilirubin higher than 5 mg/dL (HR 2.26, CI 95%: 1.39-3.70, p < .001) were independent prognostic factors of 2-year mortality, whereas cirrhosis was not.

CONCLUSIONS

Chemotherapy should be proposed only in patients with compensated cirrhosis with close monitoring of liver function. Dose management remains challenging. Multidisciplinary management is warranted to improve these patients' outcomes.

Evaluation of Renal Impairment Influence on Metabolic Drug Clearance using a Modelling Approach

BACKGROUND AND OBJECTIVE

Chronic kidney disease (CKD) may alter drug renal elimination but is also known for interacting with hepatic metabolism via multiple uremic components. However, few global models, considering the five major cytochromes, have been published, and none specifically address the decrease in cytochrome P450 (CYP450) activity. The aim of our study was to estimate the possibility of quantifying residual cytochrome activity as a function of filtration rate, according to the data available in the literature.

METHODS

For each drug in the DDI-predictor database, we collected available pharmacokinetic data comparing drug exposition in the healthy patient and in various stages of CKD, before building a model capable of predicting the variation of exposure according to the degree of renal damage. We followed an In vivo Mechanistic Static Model (IMSM) approach, previously validated for predicting change in liver clearance. We estimated the remaining fraction parameters at glomerular filtration rate (GFR) = 0 and the alpha value of GFR to 50% impairment for the 5 major cytochromes using a non-linear constrained regression using Matlab software.

RESULTS

Thirty-one compounds had usable pharmacokinetic data, with 51 AUC ratios between healthy and renal impaired patients. The remaining CYP3A4 activity was estimated to be 0.4 when CYP2D6, 2C9, 2C19 and 1A2 activity was estimated to be 0.43; 1; 0.73 and 0.7, respectively. The alpha value was estimated to be at 6.62; 25; 9.8; 1.38 and 11.04 for each cytochrome. In comparison with published data, all estimates but one were correctly predicted in the range of 0.5-2.

CONCLUSION

Our approach was able to describe the impact of CKD on metabolic elimination. Modelling this process makes it possible to anticipate changes in clearance and drug exposure in CKD patients, with the advantage of greater simplicity than approaches based on physiologically-based pharmacokinetic modelling. However, a precise estimation of the impact of renal failure is not possible with an IMSM approach due to the large variability of the published data, and thus should rely on specific pharmacokinetic modelling for narrow therapeutic margin drugs.

Drug-Drug-Gene Interactions in Cardiovascular Medicine

Cardiovascular disease remains a leading cause of both morbidity and mortality worldwide. It is widely accepted that both concomitant medications (drug-drug interactions, DDIs) and genomic factors (drug-gene interactions, DGIs) can influence cardiovascular drug-related efficacy and safety outcomes. Although thousands of DDI and DGI (aka pharmacogenomic) studies have been published to date, the literature on drug-drug-gene interactions (DDGIs, cumulative effects of DDIs and DGIs) remains scarce. Moreover, multimorbidity is common in cardiovascular disease patients and is often associated with polypharmacy, which increases the likelihood of clinically relevant drug-related interactions. These, in turn, can lead to reduced drug efficacy, medication-related harm (adverse drug reactions, longer hospitalizations, mortality) and increased healthcare costs. To examine the extent to which DDGIs and other interactions influence efficacy and safety outcomes in the field of cardiovascular medicine, we review current evidence in the field. We describe the different categories of DDIs and DGIs before illustrating how these two interact to produce DDGIs and other complex interactions. We provide examples of studies that have reported the prevalence of clinically relevant interactions and the most implicated cardiovascular medicines before outlining the challenges associated with dealing with these interactions in clinical practice. Finally, we provide recommendations on how to manage the challenges including but not limited to expanding the scope of drug information compendia, interaction databases and clinical implementation guidelines (to include clinically relevant DDGIs and other complex interactions) and work towards their harmonization; better use of electronic decision support tools; using big data and novel computational techniques; using clinically relevant endpoints, preemptive genotyping; ensuring ethnic diversity; and upskilling of clinicians in pharmacogenomics and personalized medicine.

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools’ respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools’ respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (R_AUC) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM (The National Agency for the Safety of Medicines and Health Products), GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P’s R_AUC: 90.0%, 44.2% and 75.0% for R_AUC ≤ 0.5; R_AUC 0.5–2 and R_AUC > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the R_AUC category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.

Evidence-Based Recommendations to Improve the Safe Use of Drugs in Patients with Liver Cirrhosis

Introduction

The presence of liver cirrhosis can have a major impact on pharmacodynamics and pharmacokinetics, but guidance for prescribing is lacking.

Objective

The aim of this study is to provide an overview of evidence-based recommendations developed for the safe use of drugs in liver cirrhosis.

Methods

Recommendations were based on a systematic literature search combined with expert opinion from a panel of 10 experts. The safety of each drug was classified as safe, no additional risks known, additional risks known, unsafe, unknown or the safety class was dependent on the severity of liver cirrhosis (Child–Pugh classification). If applicable, drug-specific dosing advice was provided. All recommendations were implemented in clinical decision support systems and on a website.

Results

We formulated 218 recommendations for a total of 209 drugs. For nine drugs, two recommendations were formulated for different administration routes or indications. Drugs were classified as ‘safe’ in 29 recommendations (13.3%), ‘no additional risks known’ in 60 (27.5%), ‘additional risks known’ in 3 (1.4%), and ‘unsafe’ in 30 (13.8%). In 57 (26.1%) of the recommendations, safety depended on the severity of liver cirrhosis and was ‘unknown’ in 39 (17.9%) recommendations. Large alterations in pharmacodynamics were the main reason for classifying a drug as ‘unsafe’. For 67 drugs (31%), a dose adjustment was needed.

Conclusions

Over 200 recommendations were developed for the safe use of drugs in patients with liver cirrhosis. Implementing these recommendations into clinical practice can possibly enhance medication safety in this vulnerable patient group.

Electronic supplementary material

The online version of this article (10.1007/s40264-017-0635-x) contains supplementary material, which is available to authorized users.

Complex Drug-Drug-Gene-Disease Interactions Involving Cytochromes P450: Systematic Review of Published Case Reports and Clinical Perspectives

Drug pharmacokinetics (PK) is influenced by multiple intrinsic and extrinsic factors, among which concomitant medications are responsible for drug-drug interactions (DDIs) that may have a clinical relevance, resulting in adverse drug reactions or reduced efficacy. The addition of intrinsic factors affecting cytochromes P450 (CYPs) activity and/or expression, such as genetic polymorphisms and diseases, may potentiate the impact and clinical relevance of DDIs. In addition, greater variability in drug levels and exposures has been observed when such intrinsic factors are present in addition to concomitant medications perpetrating DDIs. This variability results in poor predictability of DDIs and potentially dramatic clinical consequences. The present review illustrates the issue of complex DDIs using systematically searched published case reports of DDIs involving genetic polymorphisms, renal impairment, cirrhosis, and/or inflammation. Current knowledge on the impact of each of these factors on drug exposure and DDIs is summarized and future perspectives for the management of such complex DDIs in clinical practice are discussed, including the use of advanced Computerized Physician Order Entry (CPOE) systems, the development of model-based dose optimization strategies, and the education of healthcare professionals with respect to personalized medicine.

Safe use of proton pump inhibitors in patients with cirrhosis

Aims

Proton pump inhibitors (PPIs) belong to the most frequently used drugs, also in patients with cirrhosis. PPIs are extensively metabolized by the liver, but practice guidance on prescribing in cirrhosis is lacking. We aim to develop practical guidance on the safe use of PPIs in patients with cirrhosis.

Methods

A systematic literature search identified studies on the safety (i.e. adverse events) and pharmacokinetics of PPIs in cirrhotic patients. This evidence and data from the product information was reviewed by an expert panel who classified drugs as safe; no additional risks known; additional risks known; unsafe; or unknown. Guidance was aimed at the oral use of PPIs and categorized by the severity of cirrhosis, using the Child–Turcotte–Pugh (CTP) classification.

Results

A total of 69 studies were included. Esomeprazole, omeprazole and rabeprazole were classified as having ‘no additional risks known’. A reduction in maximum dose of omeprazole and rabeprazole is recommended for CTP A and B patients. For patients with CTP C cirrhosis, the only PPI advised is esomeprazole at a maximum dosage of 20 mg per day. Pantoprazole and lansoprazole were classified as unsafe because of 4‐ to 8‐fold increased exposure. The use of PPIs in cirrhotic patients has been associated with the development of infections and hepatic encephalopathy and should be carefully considered.

Conclusions

We suggest using esomeprazole, omeprazole or rabeprazole in patients with CTP A or B cirrhosis and only esomeprazole in patients with CTP C. Pharmacokinetic changes are also important to consider when prescribing PPIs to vulnerable, cirrhotic patients.

Evaluating the safety and dosing of drugs in patients with liver cirrhosis by literature review and expert opinion

INTRODUCTION

Liver cirrhosis can have a major impact on drug pharmacokinetics and pharmacodynamics. Patients with cirrhosis often suffer from potentially preventable adverse drug reactions. Guidelines on safe prescribing for these patients are lacking. The aim of this study is to develop a systematic method for evaluating the safety and optimal dosage of drugs in patients with liver cirrhosis.

METHODS AND ANALYSIS

For each drug, a six-step evaluation process will be followed. (1) Available evidence on the pharmacokinetics and safety of a drug in patients with liver cirrhosis will be collected from the Summary of Product Characteristics (SmPC) and a systematic literature review will be performed. (2) Data regarding two outcomes, namely pharmacokinetics and safety, will be extracted and presented in a standardised assessment report. (3) A safety classification and dosage suggestion will be proposed for each drug. (4) An expert panel will discuss the validity and clinical relevance of this suggested advice. (5) Advices will be implemented in all relevant Clinical Decision Support Systems in the Netherlands and published on a website for patients and healthcare professionals. (6) The continuity of the advices will be guaranteed by a yearly check of new literature and comments on the advices. This protocol will be applied in the evaluation of a selection of drugs: (A) drugs used to treat (complications of) liver cirrhosis, and (B) drugs frequently prescribed to the general population.

ETHICS AND DISSEMINATION

Since this study does not directly involve human participants, it does not require ethical clearance. Besides implementation on a website and in clinical decision support systems, we aim to publish the generated advices of one or two drug classes in a peer-reviewed journal and at conference meetings.

Comparison of the static in vivo approach to a physiologically based pharmacokinetic approach for metabolic drug–drug interactions prediction

Background: The in vivo mechanistic static model (IMSM) and the physiologically based pharmacokinetic (PBPK) model are two approaches used to predict the magnitude of drug–drug interactions (DDIs). The aim of this study was to evaluate the performance of IMSM and to compare IMSM with the PBPK approach implemented in Simcyp. Methods: The predictive performances of IMSM were evaluated on a panel of 628 DDIs. Subsequently, the IMSM and PBPK approaches were compared on a set of 104 DDIs. Results: The IMSM yielded 85% of predictions within 1.5-fold of the observed value on the 628 DDIs panel. The predictive performances of IMSM were better than those of the PBPK approach (median fold error 1 vs 0.86 on 104 studies; p = 0.02). Conclusion: The IMSM approach is an alternative tool for metabolic DDIs prediction.