The Impact of Pharmacogenetics on Pharmacokinetics and Pharmacodynamics in Neonates and Infants: A Systematic Review

Drug related adverse event assessment in neonates in clinical trials and clinical care

INTRODUCTION

Assessment of drug-related adverse events is essential to fully understand the benefit-risk balance of any drug exposure, weighing efficacy versus safety. This is needed for both drug labeling and clinical decision-making. Assessment is based on seriousness, severity and causality, be it more difficult to apply in neonates. Adverse event detection or prevention in the neonatal clinical setting is also more complicated because of polypharmacy, and off-label or unlicensed pharmacotherapy.

AREAS COVERED

Tools became available to assess severity and causality of adverse events in neonates recruited in clinical trials. The first version of the Neonatal Adverse Event severity score (NAESS) reduced the inter-observer variability. Causality tools like the Naranjo score were also tailored to neonates. These tools are also instrumental to support proactive pharmacovigilance in clinical care, while multidisciplinary care teams and computerized pharmacovigilance using advanced data analysis, like machine learning are emerging approaches to develop effective decision strategies.

EXPERT OPINION

All stakeholders involved in development of medicines or its clinical use should be aware of the limitations of the currently available assessment tools. Extension and optimization of these tools, advanced data analysis approaches, and capturing the variability in time-dependent physiology are warranted to improve pharmacovigilance in neonates.

Pharmacokinetic and pharmacodynamic principles: unique considerations for optimal design of neonatal clinical trials

Core clinical pharmacology principles must be considered when designing and executing neonatal clinical trials. In this review, the authors discuss important aspects of drug dose selection, pharmacokinetics, pharmacogenetics and pharmacodynamics that stakeholders may consider when undertaking a neonatal or infant clinical trial.

Safety of medicines during breastfeeding - from case report to modeling : A contribution from the ConcePTION project

INTRODUCTION

Despite many research efforts, current data on the safety of medicines during breastfeeding are either fragmented or lacking, resulting in restrictive labeling of most medicines. In the absence of pharmacoepidemiologic safety studies, risk estimation for breastfed infants is mainly derived from pharmacokinetic (PK) information on the medicine. This manuscript provides a description and a comparison of the different methodological approaches that can yield reliable information on medicine transfer into human milk and the resulting infant exposure.

AREA COVERED

Currently, most information on medicine transfer in human milk relies on case reports or traditional PK studies, which generate data that can hardly be generalized to the population. Some methodological approaches, such as population PK (popPK) and physiologically-based PK (PBPK) modeling, can be used to provide a more complete characterization of infant medicine exposure through human milk and simulate the most extreme situations, while decreasing the burden of sampling in breastfeeding women.

EXPERT OPINION

PBPK and popPK modeling are promising approaches to fill the gap of knowledge in medicine safety in breastfeeding, as illustrated with our escitalopram example.

Where are the data linking infant outcomes, breastfeeding and medicine exposure? A systematic scoping review

INTRODUCTION

Information on the impact of medicines on breastfeeding and the breastfed infant remains scarce. The aims of this review were to identify databases and cohorts holding this information, and pinpoint current information and research deficits.

METHOD

We searched 12 electronic databases, including PubMed/ Medline and Scopus, using a combination of controlled vocabulary (MeSH terms) and free text terms. We included studies reporting data from databases with information on breastfeeding, medicines exposure, and infant outcomes. We excluded studies not reporting all three parameters. Two reviewers independently selected papers and extracted data using a standardised spreadsheet. Risk of bias was assessed. Recruited cohorts with relevant information were tabulated separately. Discrepancies were resolved by discussion.

RESULTS

From 752 unique records, 69 studies were identified for full review. Eleven papers reported analyses from ten established databases with information on maternal prescription or non-prescription drugs, breastfeeding and infant outcomes. Twenty-four cohort studies were also identified. No studies reported educational or long-term developmental outcomes. The data are too sparse to warrant any firm conclusions, beyond the need for more data. The overall picture hints at 1) unquantifiable, but probably rare, serious harms to infants exposed to medicines via breastmilk, 2) unknown long-term harms, and 3) a more insidious but more pervasive harm in terms of reduced breastfeeding rates following medicines exposure in late pregnancy and peri-partum.

IMPLICATIONS

Analyses of databases reporting on the full population are needed to quantify any adverse effects of medicines and identify dyads at risk of harm from prescribed medicines while breastfeeding. This information is essential to ensure 1) infants are monitored appropriately for any adverse drug reactions 2) inform breastfeeding patients using long-term medicines as to whether the benefits of breastfeeding outweigh exposure to medicines via breastmilk and 3) target additional support to breastfeeding patients whose medicines may affect breastfeeding. The protocol is registered with the Registry of Systematic Reviews, no.994.

Pharmacogenetic profiling via genome sequencing in children with medical complexity

BACKGROUND

Children with medical complexity (CMC) are a priority pediatric population, with high resource use and associated costs. Genome-wide sequencing is increasingly organized for CMC early in life as a diagnostic test. Polypharmacy becomes common as CMC age. Clinically relevant pharmacogenetic (PGx) information can be extracted from existing genome sequencing (GS) data via GS-PGx profiling. The role of GS-PGx profiling in the CMC population is unclear.

METHODS

Prescribed medications were extracted from care plans of 802 eligible CMC enrolled in a structured Complex Care Program over a 10-year period. Drug-gene associations were annotated using curated Clinical Pharmacogenetics Implementation Consortium data. GS-PGx profiling was then performed for a subset of 50 CMC.

RESULTS

Overall, 546 CMC (68%) were prescribed at least one medication with an established PGx association. In the GS-PGx subgroup, 24 (48%) carried variants in pharmacogenes with drug-gene guidelines for one or more of their current medications. All had findings of potential relevance to some medications, including 32 (64%) with variants in CYP2C19 that could affect their metabolism of proton-pump inhibitors.

CONCLUSION

GS-PGx profiling at the time of diagnostics-focused genetic testing could be an efficient way to incorporate precision prescribing practices into the lifelong care of CMC.

IMPACT

Polypharmacy and genetic test utilization are both common in children with medical complexity. The role of repurposing genome sequencing data for pharmacogenetic profiling in children with medical complexity was previously unclear. We identified a high rate of medication use with clinically relevant drug-gene associations in this priority pediatric population and demonstrated that relevant pharmacogenetic information can be extracted from their existing genome sequencing data. Pharmacogenetic profiling at the time of diagnostics-focused genetic testing could be an efficient way to incorporate precision prescribing practices into the lifelong care of children with medical complexity.