Ashley M. Hopkins

Interactive Pharmacometric Applications Using R and the Shiny Package

Interactive applications, developed using Shiny for the R programming language, have the potential to revolutionize the sharing and communication of pharmacometric model simulations. Shiny allows customization of the applications user‐interface to provide an elegant environment for displaying user‐input controls and simulation output–where the latter simultaneously updates with changing input. The flexible nature of the R language makes simulations of population variability possible thus promoting the combination of Shiny with R in model visualization.

Predicting response and toxicity to immune checkpoint inhibitors using routinely available blood and clinical markers

Immune checkpoint inhibitors (ICI) are an important development in the treatment of advanced cancer. A substantial proportion of patients treated with ICI do not respond, and additionally patients discontinue treatment due to adverse effects. While many novel biological markers related to the specific mechanisms of ICI actions have been investigated, there has also been considerable research to identify routinely available blood and clinical markers that may predict response to ICI therapy. If validated, these markers have the advantage of being easily integrated into clinical use for nominal expense. Several markers have shown promise, including baseline and post-treatment changes in leucocyte counts, lactate dehydrogenase and C-reactive protein. While promising, the results between studies have been inconsistent due to small sample sizes, follow-up time and variability in the assessed markers. To date, research on routinely available blood and clinical markers has focussed primarily on ICI use in melanoma, the use of ipilimumab and on univariate associations, but preliminary evidence is emerging for other cancer types, other ICIs and for combining markers in multivariable clinical prediction models.

Semiphysiologically Based Pharmacokinetic Model of Leflunomide Disposition in Rheumatoid Arthritis Patients

A semiphysiologically based pharmacokinetic (semi‐PBPK) population model was used to evaluate the influence of enterohepatic recycling and protein binding, as well as the effect of genetic variability in CYP1A2, CYP2C19, and ABCG2, on the large interindividual variability of teriflunomide (active metabolite) concentrations following leflunomide administration in rheumatoid arthritis (RA) patients. The model was developed with total and free teriflunomide concentrations determined in RA patients taking leflunomide, as well as mean teriflunomide concentrations following the administration of leflunomide or teriflunomide extracted from the literature. Once developed, the 15‐compartment model was able to predict total and free teriflunomide concentrations and was used to screen demographic and genotypic covariates, of which only fat‐free mass and liver function (ALT) improved prediction. This approach effectively evaluated the effects of multiple covariates on both total and free teriflunomide concentrations, which have only been explored previously through simplistic one‐compartment models for total teriflunomide.

The rheumatoid arthritis susceptibility polymorphism PTPN22 C1858T is not associated with leflunomide response or toxicity

A common polymorphism (C1858T) in the gene that encodes the protein tyrosine phosphatase non‐receptor type 22 (PTPN22) is associated with altered T‐cell responses and increased susceptibility to rheumatoid arthritis (RA) and other autoimmune diseases. Teriflunomide, the active metabolite of leflunomide, reduces T‐cell responses through inhibition of tyrosine kinase p56LCK. We examined a potential association between PTPN22 genotype and response or toxicity to leflunomide in Caucasian RA patients taking leflunomide in combination with other disease‐modifying antirheumatic drugs (DMARDs).

Genetic polymorphism of CYP1A2 but not total or free teriflunomide concentrations is associated with leflunomide cessation in rheumatoid arthritis.

AIM Leflunomide, via its active metabolite teriflunomide, is used in rheumatoid arthritis (RA) treatment, yet approximately 20 to 40% of patients cease due to toxicity. The aim was to develop a time-to-event model describing leflunomide cessation due to toxicity within a clinical cohort and to investigate potential predictors of cessation such as total and free teriflunomide exposure and pharmacogenetic influences. METHODS This study included individuals enrolled in the Early Arthritis inception cohort at the Royal Adelaide Hospital between 2000 and 2013 who received leflunomide. A time-to-event model in nonmem was used to describe the time until leflunomide cessation and the influence of teriflunomide exposure and pharmacogenetic variants. Random censoring of individuals was simultaneously described. The clinical relevance of significant covariates was visualized via simulation. RESULTS Data from 105 patients were analyzed, with 34 ceasing due to toxicity. The baseline dropout hazard and baseline random censoring hazard were best described by step functions changing over discrete time intervals. No statistically significant associations with teriflunomide exposure metrics were identified. Of the screened covariates, carriers of the C allele of CYP1A2 rs762551 had a 2.29 fold increase in cessation hazard compared with non-carriers (95% CI 2.24, 2.34, P = 0.016). CONCLUSIONS A time-to-event model described the time between leflunomide initiation and cessation due to side effects. The C allele of CYP1A2 rs762551 was linked to increased leflunomide toxicity, while no association with teriflunomide exposure was identified. Future research should continue to investigate exposure-toxicity relationships, as well as potentially toxic metabolites.

Individualization of leflunomide dosing in rheumatoid arthritis patients

Leflunomide is largely considered to be a second-line treatment option for rheumatoid arthritis (RA). Those who fail to respond, tend to progress to treatment with expensive biological agents, which can also be associated with serious toxicities. Optimizing leflunomide treatment to meet the needs of individuals would hence be beneficial in terms of patient outcomes and health care expenditure. In this respect, therapeutic drug monitoring (TDM) may be useful, as plasma concentrations of leflunomides active metabolite, teriflunomide, correlate with response to treatment, but are highly variable between patients. A number of pharmacogenetic markers have also been identified that influence response and toxicity. Incorporation of these findings into clinical practice could facilitate more efficient use of leflunomide.

Ten years of publicly funded biological disease-modifying antirheumatic drugs in Australia.

Biological disease‐modifying antirheumatic drugs (bDMARDs) for rheumatoid arthritis (RA) treatment were among the first high‐cost medicines to be subsidised in Australia. High‐cost medicines pose several challenges to the Australian National Medicines Policy, which aims to provide timely access to effective medicines at a cost individuals and the community can afford. Thus, novel restriction criteria were developed to encourage cost‐effective use of bDMARDs. Government expenditure on bDMARD subsidies for RA treatment grew to about

Optimized Cocktail Phenotyping Study Protocol Using Physiological Based Pharmacokinetic Modeling and In silico Assessment of Metabolic Drug-Drug Interactions Involving Modafinil.

383 million in 2014. Evidence that initiation and continuation criteria for bDMARDs meet usually applied cost–benefit criteria is lacking. The combined expenditure on tocilizumab, certolizumab pegol and golimumab (added to the Australian Governments Pharmaceutical Benefits Scheme in 2010) was

Intracellular CD3+ T Lymphocyte Teriflunomide Concentration Is Poorly Correlated with and Has Greater Variability Than Unbound Plasma Teriflunomide Concentration

93 million in 2014, which is 210% over the initial estimate. Present and future challenges with regard to bDMARDs for RA and other high‐cost drugs include improved expenditure predictions, monitoring of cost‐effectiveness in relation to actual use and strategic development, regulation and use of biosimilars. Ten years of documentation on clinical and laboratory findings indicating eligibility to initiate and continue on bDMARDs remains un‐used. These data represent an untapped opportunity to promote quality of use of bDMARDs and biosimilars and to improve cost predictions for high‐cost drugs.

Population Pharmacokinetic Model of Doxycycline Plasma Concentrations Using Pooled Study Data

In vivo cocktail pathway phenotyping (ICPP) is routinely used to assess the metabolic drug–drug interaction (mDDI) potential of new drug candidates (NDC) during drug development. However, there are a number of potential limitations to this approach and the use of validated drug cocktails and study protocols is essential. Typically ICPP mDDI studies assess only the impact of interactions following multiple postulated perpetrator doses and hence the emphasis in terms of validation of these studies has been ensuring that there are no interactions between probe substrates. Studies assessing the comparative impact of single and multiple doses of the postulated perpetrator have the potential to provide richer information regarding both the clinical impact and mechanism of mDDIs. Using modafinil as a model compound, we sought to develop an optimized ICPP mDDI study protocol to evaluate the potential magnitude and clinical relevance of mDDIs using a physiologically based pharmacokinetic modeling approach.