Defining drug response for stratified medicine

Adverse Drug Events in Ambulatory Care: A Cross-Sectional Study

BACKGROUND

Adverse drug events (ADEs) are understudied in the ambulatory care setting. We aim to estimate the prevalence and characteristics of ADEs in outpatient care using electronic health records (EHRs).

METHODS

This cross-sectional study included EHR data for patients who had an outpatient encounter at an academic medical center from 1 October 2018 through 31 December 2019. We developed a stratified sampling strategy based on a comprehensive set of 994 ADE-related International Classification of Disease (ICD-10) codes to identify clinical encounters and notes likely to contain ADEs. Within each ICD-10 likelihood group, clinical notes were randomly sampled and annotated for present or possible ADE-drug relationships and severity. The overall estimated population prevalence of ADEs presenting in the outpatient setting was calculated. The generalizability of the findings was assessed by comparing ICD-10 code frequencies against a large commercial database.

RESULTS

The study included 3126 notes (unique patient encounters) from 2882 unique patients. Of these, 1383 patient encounters (44.2%) had a present or possible ADE documented (6308 mentions). Of the 6038 ADEs mentioned, 14.1% were hypersensitivity reactions, 1.1% were life-threatening, 22.4% were serious, and 60.4% were significant. Main causal agents included anti-infectives (19.3%), central nervous system agents (12.8%), and cardiovascular agents (11.5%). The overall prevalence of present ADEs mentioned in the clinical notes was estimated to be 1.97 per 100 patient encounters (or 2.52 per 100 patient encounters when possible ADEs are included).

CONCLUSIONS

This study identified the overall population prevalence per encounter of ADEs in the outpatient population by leveraging ICD-10 codes and investigating ADEs documented in clinical notes. Understanding the ADE characteristics in a large corpus of outpatient documentation advances pharmacovigilance knowledge, enhancing the detection, monitoring, and prevention of ADEs in ambulatory care.

Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials

Dementia probably due to Alzheimer's disease is a progressive condition that manifests in cognitive decline and impairs patients' daily life. Affected patients show great heterogeneity in their symptomatic progression, which hampers the identification of efficacious treatments in clinical trials. Using artificial intelligence approaches to enable clinical enrichment trials serves a promising avenue to identify treatments. In this work, we used a deep learning method to cluster the multivariate disease trajectories of 283 early dementia patients along cognitive and functional scores. Two distinct subgroups were identified that separated patients into 'slow' and 'fast' progressing individuals. These subgroups were externally validated and independently replicated in a dementia cohort comprising 2779 patients. We trained a machine learning model to predict the progression subgroup of a patient from cross-sectional data at their time of dementia diagnosis. The classifier achieved a prediction performance of 0.70 ± 0.01 area under the receiver operating characteristic curve in external validation. By emulating a hypothetical clinical trial conducting patient enrichment using the proposed classifier, we estimate its potential to decrease the required sample size. Furthermore, we balance the achieved enrichment of the trial cohort against the accompanied demand for increased patient screening. Our results show that enrichment trials targeting cognitive outcomes offer improved chances of trial success and are more than 13% cheaper compared with conventional clinical trials. The resources saved could be redirected to accelerate drug development and expand the search for remedies for cognitive impairment.

Frequency and preventability of adverse drug events in the outpatient setting

BACKGROUND

Limited data exist regarding adverse drug events (ADEs) in the outpatient setting. The objective of this study was to determine the incidence, severity, and preventability of ADEs in the outpatient setting and identify potential prevention strategies.

METHODS

We conducted an analysis of ADEs identified in a retrospective electronic health records review of outpatient encounters in 2018 at 13 outpatient sites in Massachusetts that included 13 416 outpatient encounters in 3323 patients. Triggers were identified in the medical record including medications, consultations, laboratory results, and others. If a trigger was detected, a further in-depth review was conducted by nurses and adjudicated by physicians to examine the relevant information in the medical record. Patients were included in the study if they were at least 18 years of age with at least one outpatient encounter with a physician, nurse practitioner or physician's assistant in that calendar year. Patients were excluded from the study if the outpatient encounter occurred in outpatient surgery, psychiatry, rehabilitation, and paediatrics.

RESULTS

In all, 5% of patients experienced an ADE over the 1-year period. We identified 198 ADEs among 170 patients, who had a mean age of 60. Most patients experienced one ADE (87%), 10% experienced two ADEs and 3% experienced three or more ADEs. The most frequent drug classes resulting in ADEs were cardiovascular (25%), central nervous system (14%), and anti-infective agents (14%). Severity was ranked as significant in 85%, 14% were serious, 1% were life-threatening, and there were no fatal ADEs. Of the ADEs, 22% were classified as preventable and 78% were not preventable. We identified 246 potential prevention strategies, and 23% of ADEs had more than one prevention strategy possibility.

CONCLUSIONS

Despite efforts to prioritise patient safety, medication-related harms are still frequent. These results underscore the need for further patient safety improvement in the outpatient setting.

Understanding the impact of ABCG2 polymorphisms on drug pharmacokinetics: focus on rosuvastatin and allopurinol

INTRODUCTION

In addition to the well-established understanding of the pharmacogenetics of drug-metabolizing enzymes, there is growing data on the effects of genetic variation in drug transporters, particularly ATP-binding cassette (ABC) transporters. However, the evidence that these genetic variants can be used to predict drug effects and to adjust individual dosing to avoid adverse events is still limited.

AREAS COVERED

This review presents a summary of the current literature from the PubMed database as of February 2024 regarding the impact of genetic variants on ABCG2 function and their relevance to the clinical use of the HMG-CoA reductase inhibitor rosuvastatin and the xanthine oxidase inhibitor allopurinol.

EXPERT OPINION

Although there are pharmacogenetic guidelines for the ABCG2 missense variant Q141K, there is still some conflicting data regarding the clinical benefits of these recommendations. Some caution appears to be warranted in homozygous ABCG2 Q141K carriers when rosuvastatin is administered at higher doses and such information is already included in the drug label. The benefit of dose adaption to lower possible side effects needs to be evaluated in prospective clinical studies.

Of Mice and Men: The Inter-individual Variability of the Brain's Response to Drugs

Biological variation is ubiquitous in nature. Despite highly standardized breeding and husbandry under controlled environmental conditions, phenotypic diversity exists in laboratory mice and rats just as it does in humans. The resulting inter-individual variability affects various characteristics of animal disease models, including the responsiveness to drugs. Thus, the common practice of averaging data within an experimental group can lead to misinterpretations in neuroscience and other research fields. In this commentary, the impact of inter-individual variation in drug responsiveness is illustrated by examples from the testing of antiseizure medications in rodent temporal lobe epilepsy models. Individual mice and rats rendered epileptic by treatment according to standardized protocols fall into groups that either do or do not respond to antiseizure medications, thus mimicking the clinical situation in patients with epilepsy. Population responses are not normally distributed, and divergent responding is concealed in averages subjected to parametric statistical tests. Genetic, epigenetic, and environmental factors are believed to contribute to inter-individual variation in drug response but the specific molecular and physiological causes are not well understood. Being aware of inter-individual variability in rodents allows an improved interpretation of both behavioral phenotypes and drug effects in a pharmacological experiment.

Precision antiplatelet therapy

A State of the Art lecture titled "Personalizing Antiplatelet Therapy Based on Platelet Turnover and Metabolic Phenotype" was presented by Bianca Rocca at the International Society on Thrombosis and Haemostasis (ISTH) Congress in 2022. Increased variability in drug response may be associated with serious, mechanism-based and off-target side effects, especially in the case of drugs that do not routinely undergo therapeutic drug monitoring, such as antiplatelet drugs or direct oral anticoagulants. Precision pharmacology can be defined as the identification of a drug regimen that maximizes the benefit/risk balance at the level of an individual patient. Key tools for identifying relevant sources of variability and developing precision drug dosing are represented by genetic, biochemical, and pharmacological biomarkers recognized as a valid surrogate or strong predictor of major clinical complications. Pharmacodynamic, pharmacokinetic, and/or disease-related biomarkers are central to identifying the right population to be targeted, characterizing the sources of variability in drug response, guiding precision treatments that maximize benefits and minimize risks, and designing precision dosing trials. Another valuable tool for guiding precision pharmacology is represented by in silico pharmacokinetic/pharmacodynamic models and simulations instructed by real-world data of validated biomarkers. This review critically analyzes the tools for precision dosing and exemplifies conditions in which precision dosing can considerably optimize the efficacy and safety of antiplatelet drugs, namely aspirin and P2Y12 receptor blockers, used alone and in combination. Finally, we summarize relevant new data on this topic presented during the 2022 ISTH Congress.

The gut microbiome–Does stool represent right?

Many stool-based gut microbiome studies have highlighted the importance of the microbiome. However, we hypothesized that stool is a poor proxy for the inner-colonic microbiome and that studying stool samples may be inadequate to capture the true inner-colonic microbiome. To test this hypothesis, we conducted prospective clinical studies with up to 20 patients undergoing an FDA-cleared gravity-fed colonic lavage without oral purgative pre-consumption. The objective of this study was to present the analysis of inner-colonic microbiota obtained non-invasively during the lavage and how these results differ from stool samples. The inner-colonic samples represented the descending, transverse, and ascending colon. All samples were analyzed for 16S rRNA and shotgun metagenomic sequences. The taxonomic, phylogenetic, and biosynthetic gene cluster analyses showed a distinctive biogeographic gradient and revealed differences between the sample types, especially in the proximal colon. The high percentage of unique information found only in the inner-colonic effluent highlights the importance of these samples and likewise the importance of collecting them using a method that can preserve these distinctive signatures. We proposed that these samples are imperative for developing future biomarkers, targeted therapeutics, and personalized medicine.

Pharmacogenomics: current status and future perspectives

Inter-individual variability in drug response, be it efficacy or safety, is common and likely to become an increasing problem globally given the growing elderly population requiring treatment. Reasons for this inter-individual variability include genomic factors, an area of study called pharmacogenomics. With genotyping technologies now widely available and decreasing in cost, implementing pharmacogenomics into clinical practice - widely regarded as one of the initial steps in mainstreaming genomic medicine - is currently a focus in many countries worldwide. However, major challenges of implementation lie at the point of delivery into health-care systems, including the modification of current clinical pathways coupled with a massive knowledge gap in pharmacogenomics in the health-care workforce. Pharmacogenomics can also be used in a broader sense for drug discovery and development, with increasing evidence suggesting that genomically defined targets have an increased success rate during clinical development.

Stratified glucose-lowering response to vildagliptin and pioglitazone by obesity and hypertriglyceridemia in a randomized crossover trial

Background

Understanding which group of patients with type 2 diabetes will have the most glucose lowering response to certain medications (which target different aspects of glucose metabolism) is the first step in precision medicine.

Aims

We hypothesized that people with type 2 diabetes who generally have high insulin resistance, such as people of Māori/Pacific ethnicity, and those with obesity and/or hypertriglyceridemia (OHTG), would have greater glucose-lowering by pioglitazone (an insulin sensitizer) versus vildagliptin (an insulin secretagogue).

Methods

A randomised, open-label, two-period crossover trial was conducted in New Zealand. Adults with type 2 diabetes, HbA1c>58mmol/mol (>7.5%), received 16 weeks of either pioglitazone (30mg) or vildagliptin (50mg) daily, then switched to the other medication over for another 16 weeks of treatment. Differences in HbA1c were tested for interaction with ethnicity or OHTG, controlling for baseline HbA1c using linear mixed models. Secondary outcomes included weight, blood pressure, side-effects and diabetes treatment satisfaction.

Results

346 participants were randomised (55% Māori/Pacific) between February 2019 to March 2020. HbA1c after pioglitazone was lower than after vildagliptin (mean difference -4.9mmol/mol [0.5%]; 95% CI -6.3, -3.5; p<0.0001). Primary intention-to-treat analysis showed no significant interaction effect by Māori/Pacific vs other ethnicity (1.5mmol/mol [0.1%], 95% CI -0.8, 3.7), and per-protocol analysis (-1.2mmol/mol [0.1%], 95% CI -4.1, 1.7). An interaction effect (-4.7mmol/mol [0.5%], 95% CI -8.1, -1.4) was found by OHTG status. Both treatments generated similar treatment satisfaction scores, although there was greater weight gain and greater improvement in lipids and liver enzymes after pioglitazone than vildagliptin.

Conclusions

Comparative glucose-lowering by pioglitazone and vildagliptin is not different between Māori/Pacific people compared with other New Zealand ethnic groups. Presence of OHTG predicts greater glucose lowering by pioglitazone than vildagliptin.

Clinical trial registration

www.anzctr.org.au, identifier (ACTRN12618001907235).

The Implementation of Pharmacogenetics in the United Kingdom

There is considerable inter-individual variability in the effectiveness and safety of pharmaceutical interventions. This phenomenon can be attributed to a multitude of factors; however, it is widely acknowledged that common genetic variation affecting drug absorption or metabolism play a substantial contributory role. This is a concept known as pharmacogenetics. Understanding how common genetic variants influence responses to medications, and using this knowledge to inform prescribing practice, could yield significant advantages for both patients and healthcare systems. Some health services around the world have introduced pharmacogenetics into routine practice, whereas others are less advanced along the implementation pathway. This chapter introduces the field of pharmacogenetics, the existing body of evidence, and discusses barriers to implementation. The chapter will specifically focus on efforts to introduce pharmacogenetics in the NHS, highlighting key challenges related to scale, informatics, and education.

How paediatric drug development and use could benefit from OMICs: a c4c expert group white paper

The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates, and infants, is limited by a paucity of good quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OMICs technologies may support these efforts, by complementary information about targeted and non-targeted molecules through systematic characterization and quantitation of biological samples. OMICs technologies comprise at least genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics in addition to the patient's phenotype. OMICs technologies are in part hypothesis-generating allowing an in depth understanding of disease pathophysiology and pharmacological mechanisms. Application of OMICs technologies in paediatrics faces major challenges before routine adoption. First, developmental processes need to be considered, including a sub-division into specific age groups as developmental changes clearly impact OMICs data. Second, compared to the adult population, the number of patients is limited as well as type and amount of necessary biomaterial, especially in neonates and preterms. Thus, advanced trial designs and biostatistical methods, non-invasive biomarkers, innovative biobanking concepts including data and samples from healthy children, as well as analytical approaches (e.g. liquid biopsies) should be addressed to overcome these obstacles. The ultimate goal is to link OMICs technologies with innovative analysis tools, like artificial intelligence at an early stage. The use of OMICs data based on a feasible approach will contribute to identify complex phenotypes and subpopulations of patients to improve development of medicines for children with potential economic advantages.

ABCB1, ABCG2, ABCC1, ABCC2, and ABCC3 drug transporter polymorphisms and their impact on drug bioavailability: what is our current understanding?

INTRODUCTION

Interindividual differences in drug response are a frequent clinical challenge partly due to variation in pharmacokinetics. ATP-binding cassette (ABC) transporters are crucial determinants of drug disposition. They are subject of gene regulation and drug-interaction; however, it is still under debate to which extend genetic variants in these transporters contribute to interindividual variability of a wide range of drugs.

AREAS COVERED

This review discusses the current literature on the impact of genetic variants in ABCB1, ABCG2 as well as ABCC1, ABCC2, and ABCC3 on pharmacokinetics and drug response. The aim was to evaluate if results from recent studies would increase the evidence for potential clinically relevant pharmacogenetic effects.

EXPERT OPINION

Although enormous efforts have been made to investigate effects of ABC transporter genotypes on drug pharmacokinetics and response, the majority of studies showed only weak if any associations. Despite few unique results, studies mostly failed to confirm earlier findings or still remained inconsistent. The impact of genetic variants on drug bioavailability is only minor and other factors regulating the transporter expression and function seem to be more critical. In our opinion, the findings on the so far investigated genetic variants in ABC efflux transporters are not suitable as predictive biomarkers.

Nursing Personnel in the Era of Personalized Healthcare in Clinical Practice

Personalized, stratified, or precision medicine (PM) introduces a new era in healthcare that tries to identify and predict optimum treatment outcomes for a patient or a cohort. It also introduces new scientific terminologies regarding therapeutic approaches and the need of their adoption from healthcare providers. Till today, evidence-based practice (EBP) was focusing on population averages and their variances among cohorts for clinical values that are essential for optimizing healthcare outcome. It can be stated that EBP and PM are complementary approaches for a modern healthcare system. Healthcare providers through EBP often see the forest (population averages) but miss the trees (individual patients), whereas utilization of PM may not see the forest for the trees. Nursing personnel (NP) play an important role in modern healthcare since they are consulting, educating, and providing care to patients whose needs often needs to be individualized (personalized nursing care, PNC). Based on the clinical issues earlier addressed from clinical pharmacology, EBP, and now encompassed in PM, this review tries to describe the challenges that NP have to face in order to meet the requisites of the new era in healthcare. It presents the demands that should be met for upgrading the provided education and expertise of NP toward an updated role in a modern healthcare system.

Demonstrating Heterogeneity of Treatment Effects Among Patients: An Overlooked but Important Step Toward Precision Medicine

Although heterogeneity in the observed outcomes in clinical trials is often assumed to reflect a true heterogeneous response, it could actually be due to random variability. This retrospective analysis of four randomized, double-blind, placebo-controlled multiperiod (i.e., episode) crossover trials of fentanyl for breakthrough cancer pain illustrates the use of multiperiod crossover trials to examine heterogeneity of treatment response. A mixed-effects model, including fixed effects for treatment and episode and random effects for patient and treatment-by-patient interaction, was used to assess the heterogeneity in patients' responses to treatment during each episode. A significant treatment-by-patient interaction was found for three of four trials (P < 0.05), suggesting heterogeneity of the effect of fentanyl among different patients in each trial. Similar analyses in other therapeutic areas could identify conditions and therapies that should be investigated further for predictors of treatment response in efforts to maximize the efficiency of developing precision medicine strategies.

Does understanding endotypes translate to better asthma management options for all?

Despite the development of novel treatments, improvement in the design of delivery devices, and new technologies for monitoring and improving adherence, the burden of asthma is not decreasing. Predicting an individual patient's response to asthma drugs remains challenging, and the provision of personalized treatment remains elusive. Although biomarkers, such as allergic sensitization and blood eosinophilia, might be important predictors of response to inhaled corticosteroids in preschool children, these relatively cheap and available investigations are seldom used in clinical practice to select patients for corticosteroid prescription. However, for the majority of patients, response to different treatments cannot be accurately predicted. One of the key factors preventing further advances is the reductionist view of asthma as a single disease, which is forcing patients with different asthma subtypes into a single group for empiric treatment. This inevitably results in treatment failures and, for some, an unacceptable risk/benefit ratio. The approach to asthma today is an example of the traditional symptom (diagnosis)-based, one-size-fits-all approach rather than a stratified approach, and our guidelines-driven management based on a unitary diagnosis might not be the optimal way to deliver care. The only way to deliver stratified medicine and find a cure is through the understanding of asthma endotypes. We propose that the way to discover endotypes, biomarkers, and personalized treatments is through the iterative process based on interpretation of big data analytics from birth and patient cohorts, responses to treatments in randomized controlled trials, and in vitro mechanistic studies using human samples and experimental animal models, with technological and methodological advances at its core.

Precision Medicine

Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime which comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, timing of administration, specific diet or exercise recommendation, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes which maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges.

An integrative systems biology approach for precision medicine in diabetic kidney disease

Current therapeutic approaches are ineffective in many patients with established diabetic kidney disease (DKD), an epidemic affecting one in three patients with diabetes. Early identification of patients at high risk for progression and individualizing therapies have the potential to mitigate kidney complications due to diabetes. To achieve this, a better understanding of the complex pathophysiology of DKD is needed. A system biology approach integrating large-scale omic data is well suited to unravel the molecular mechanisms driving DKD and may offer new perspectives how to personalize therapy. Recent studies indeed show that integrating genome scale data sets generated from prospectively designed clinical cohort studies with model systems using innovative bioinformatics analysis revealed critical molecular pathways in DKD and led to the development of candidate prognostic molecular biomarkers. This review seeks to provide an overview of the recent progress in the application of the integrative systems biology approaches specifically in the field of molecular biomarkers for DKD. We will mainly focus the discussion on how to use integrative system biology approach to first identify patients at high risk of progression, and second to identify patients who may or may not respond to treatment. Challenges and opportunities in applying precision medicine in DKD will also be discussed.

Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials

BACKGROUND

IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers.

METHOD

The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project's more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages' output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials.

RESULTS

The results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl's work as well as relating important methodologies by IDeAl's definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials.

CONCLUSION

IDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.

Costs and Treatment Pathways for Type 2 Diabetes in the UK: A Mastermind Cohort Study

INTRODUCTION

Medication therapy for type 2 diabetes has become increasingly complex, and there are few reliable data on the current state of clinical practice. We report treatment pathways and associated costs of medication therapy for people with type 2 diabetes in the UK, their variability and changes over time.

METHODS

Prescription and biomarker data for 7159 people with type 2 diabetes were extracted from the GoDARTS cohort study, covering the period 1989-2013. Average follow-up was 10 years. Individuals were prescribed on average 2.4 (SD: 1.2) drugs with average annual costs of £241. We calculated summary statistics for first- and second-line therapies. Linear regression models were used to estimate associations between therapy characteristics and baseline patient characteristics.

RESULTS

Average time from diagnosis to first prescription was 3 years (SD: 4.0 years). Almost all first-line therapy (98%) was monotherapy, with average annual cost of £83 (SD: £204) for 3.8 (SD: 3.5) years. Second-line therapy was initiated in 73% of all individuals, at an average annual cost of £219 (SD: £305). Therapies involving insulin were markedly more expensive than other common therapies. Baseline HbA1c was unrelated to future therapy costs, but higher average HbA1c levels over time were associated with higher costs.

CONCLUSIONS

Medication therapy has undergone substantial changes during the period covered in this study. For example, therapy is initiated earlier and is less expensive than in the past. The data provided in this study will prove useful for future modelling studies, e.g. of stratified treatment approaches.