Genetics-based pediatric warfarin dosage regimen derived using pharmacometric bridging

Bayesian model-guided antimicrobial therapy in pediatrics

Antimicrobials have transformed the practice of medicine, making life-threatening infections treatable, but determining optimal dosing, particularly in pediatric patients, remains a challenge. The lack of pediatric data can largely be traced back to pharmaceutical companies, which, until recently, were not required to perform clinical testing in pediatrics. As a result, most antimicrobial use in pediatrics is off-label. In recent years, a concerted effort (e.g., Pediatric Research Equality Act) has been made to fill these knowledge gaps, but progress is slow and better strategies are needed. Model-based techniques have been used by pharmaceutical companies and regulatory agencies for decades to derive rational individualized dosing guidelines. Historically, these techniques have been unavailable in a clinical setting, but the advent of Bayesian-model-driven, integrated clinical decision support platforms has made model-informed precision dosing more accessible. Unfortunately, the rollout of these systems remains slow despite their increasingly well documented contributions to patient-centered care. The primary goals of this work are to 1) provide a succinct, easy-to-follow description of the challenges associated with designing and implementing dose-optimization strategies; and 2) provide supporting evidence that Bayesian-model informed precision dosing can meet those challenges. There are numerous stakeholders in a hospital setting, and our intention is for this work to serve as a starting point for clinicians who recognize that these techniques are the future of modern pharmacotherapy and wish to become champions of that movement.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Height, VKORC1 1173, and CYP2C9 Genotypes Determine Warfarin Dose for Pediatric Patients with Kawasaki Disease in Southwest China

Long-term oral warfarin is recommended in pediatric Kawasaki disease patients with large coronary artery aneurysms; however, heterogeneity is considerable. This study aimed to determine variables affecting warfarin dosage in Kawasaki disease. The enrolled individuals (194 children) were divided into four groups: (1) Cases with severe coronary artery lesions (CAL) of IV to V degrees or thrombogenesis treated with oral warfarin were assigned to Group A; (2) Group B, CAL of I degrees; (3) Group C, CAL of II and III degrees cases with small or medium-sized CAL not treated with warfarin; (4) Group D, normal children without Kawasaki disease. The relevant genotypes of CYP2C9, VKORC1 (1173, - 1639, and 3730), and CYP4F2 were assessed. There were no statistically significant differences in CYP2C9, VKORC1, and CYP4F2 mutation frequencies among the 4 groups. In the 44 Group A patients, demographic features, clinical characteristics, and genotypes were recorded, and their associations with warfarin dose variability were assessed. Multivariate linear regression analysis revealed that height, VKORC1 1173, and CYP2C9 accounted for 61.2%, 7.9%, and 4.3% of dosing variability, respectively. Conclusions: Patient height is the main factor determining warfarin dosage, while genotype effects on warfarin dosage vary among studies. New formula should be defined using data obtained from children in cases with demonstrated efficacy.

Perceptions of students in health and molecular life sciences regarding pharmacogenomics and personalized medicine

BACKGROUND

Increasing evidence is demonstrating that a patient's unique genetic profile can be used to detect the disease's onset, prevent its progression, and optimize its treatment. This led to the increased global efforts to implement personalized medicine (PM) and pharmacogenomics (PG) in clinical practice. Here we investigated the perceptions of students from different universities in Bosnia and Herzegovina (BH) towards PG/PM as well as related ethical, legal, and social implications (ELSI). This descriptive, cross-sectional study is based on the survey of 559 students from the Faculties of Medicine, Pharmacy, Health Studies, Genetics, and Bioengineering and other study programs.

RESULTS

Our results showed that 50% of students heard about personal genome testing companies and 69% consider having a genetic test done. A majority of students (57%) agreed that PM represents a promising healthcare model, and 40% of students agreed that their study program is well designed for understanding PG/PM. This latter opinion seems to be particularly influenced by the field of study (7.23, CI 1.99-26.2, p = 0.003). Students with this opinion are also more willing to continue their postgraduate education in the PM (OR = 4.68, CI 2.59-8.47, p < 0.001). Furthermore, 45% of students are aware of different ethical aspects of genetic testing, with most of them (46%) being concerned about the patient's privacy.

CONCLUSIONS

Our results indicate a positive attitude of biomedical students in Bosnia and Herzegovina towards genetic testing and personalized medicine. Importantly, our results emphasize the key importance of pharmacogenomic education for more efficient translation of precision medicine into clinical practice.

Pharmacogenomics in Pediatric Patients: Towards Personalized Medicine

It is well known that drug responses differ among patients with regard to dose requirements, efficacy, and adverse drug reactions (ADRs). The differences in drug responses are partially explained by genetic variation. This paper highlights some examples of areas in which the different responses (dose, efficacy, and ADRs) are studied in children, including cancer (cisplatin), thrombosis (vitamin K antagonists), and asthma (long-acting β2 agonists). For childhood cancer, the replication of data is challenging due to a high heterogeneity in study populations, which is mostly due to all the different treatment protocols. For example, the replication cohorts of the association of variants in TPMT and COMT with cisplatin-induced ototoxicity gave conflicting results, possibly as a result of this heterogeneity. For the vitamin K antagonists, the evidence of the association between variants in VKORC1 and CYP2C9 and the dose is clear. Genetic dosing models have been developed, but the implementation is held back by the impossibility of conducting a randomized controlled trial with such a small and diverse population. For the long-acting β2 agonists, there is enough evidence for the association between variant ADRB2 Arg16 and treatment response to start clinical trials to assess clinical value and cost effectiveness of genotyping. However, further research is still needed to define the different asthma phenotypes to study associations in comparable cohorts. These examples show the challenges which are encountered in pediatric pharmacogenomic studies. They also display the importance of collaborations to obtain good quality evidence for the implementation of genetic testing in clinical practice to optimize and personalize treatment.

Prediction of Warfarin Dose in Pediatric Patients: An Evaluation of the Predictive Performance of Several Models

OBJECTIVES

The objective of this study was to evaluate the performance of pediatric pharmacogenetic-based dose prediction models by using an independent cohort of pediatric patients from a multicenter trial.

METHODS

Clinical and genetic data (CYP2C9 [cytochrome P450 2C9] and VKORC1 [vitamin K epoxide reductase]) were collected from pediatric patients aged 3 months to 17 years who were receiving warfarin as part of standard care at 3 separate clinical sites. The accuracy of 8 previously published pediatric pharmacogenetic-based dose models was evaluated in the validation cohort by comparing predicted maintenance doses to actual stable warfarin doses. The predictive ability was assessed by using the proportion of variance (R(2)), mean prediction error (MPE), and the percentage of predictions that fell within 20% of the actual maintenance dose.

RESULTS

Thirty-two children reached a stable international normalized ratio and were included in the validation cohort. The pharmacogenetic-based warfarin dose models showed a proportion of variance ranging from 35% to 78% and an MPE ranging from -2.67 to 0.85 mg/day in the validation cohort. Overall, the model developed by Hamberg et al showed the best performance in the validation cohort (R(2) = 78%; MPE = 0.15 mg/day) with 38% of the predictions falling within 20% of observed doses.

CONCLUSIONS

Pharmacogenetic-based algorithms provide better predictions than a fixed-dose approach, although an optimal dose algorithm has not yet been developed.

Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques

Purpose

Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates.

Methods

COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods.

Results

Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols.

Conclusion

Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-015-1699-x) contains supplementary material, which is available to authorized users.

A review of a priori regression models for warfarin maintenance dose prediction

A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.

Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems

We describe here the design and initial implementation of the eMERGE-PGx project. eMERGE-PGx, a partnership of the eMERGE and PGRN consortia, has three objectives : 1) Deploy PGRNseq, a next-generation sequencing platform assessing sequence variation in 84 proposed pharmacogenes, in nearly 9,000 patients likely to be prescribed drugs of interest in a 1–3 year timeframe across several clinical sites; 2) Integrate well-established clinically-validated pharmacogenetic genotypes into the electronic health record with associated clinical decision support and assess process and clinical outcomes of implementation; and 3) Develop a repository of pharmacogenetic variants of unknown significance linked to a repository of EHR-based clinical phenotype data for ongoing pharmacogenomics discovery. We describe site-specific project implementation and anticipated products, including genetic variant and phenotype data repositories, novel variant association studies, clinical decision support modules, clinical and process outcomes, approaches to manage incidental findings, and patient and clinician education methods.