Pharmacokinetic (PK)- and pharmacodynamic (PD) modeling are useful tools whenassessing treatment effect. A patient’s adherence can potentially be rate-limiting, since it isthe first process in a chain of processes that determines treatment effect. Therefore agreater system taking into consideration PKPD as well as adherence models couldpotentially unlock a greater system understanding. This study focuses on investigating thefeasibility of combining models concerning adherence, PK and PD. An extensive mapping of previously made work on the topics of PKPD model developmentand adherence models concerning type 2 diabetes was conducted. Results concluded thatthere are gaps in research regarding adequate adherence-scoring methods that easily can belinked to dosing regimens. Furthermore, there is lacking research regarding feedback fromexposure-response to adherence. A simple model was implemented to provide a proposedlinkage inhowthe connection could be made between adherence and a PKPD-model.Sensitivity analysis showed that the adherence scoring used (Summary of DiabetesSelf-Care Activities measure, SDSCA) had a moderate correlation to the final response onfasting plasma glucose (Spearman ρ=−0.478∗∗∗). This result suggests that adherenceshould be considered as a relatively important factor to weave in to systems models ofpharmacology and future research should be made on further developing modelsimplementing both social factors, such as adherence, as well as pharmacologic response. Apossible way could be linking dose regimen to adherence scoring. / Farmakokinetiska (PK)- och farmakodynamiska (PD) modeller är användbara verktyg vid utvärdering av effekten av en behandlingsplan. Patientens åtlydnad tilll läkemedelsordinationen kan potentiellt vara en begränsande faktor för behandlingsprocessen. Att utveckla större system som täcker farmakologiska- samt åtlydnadsmodeller skulle potentiellt kunna vara en väg till en förhöjd förståelse angående farmakologiska system. Denna studie fokuserar på att undersöka genomförbarheten av att koppla samman modeller angående farmakokinetik, farmakodynamik samt åtlydnadsmodeller. En omfattande kartläggning av tidigare utfört arbete angående utvecklingen av PKPD-modeller och åtlydnadsmodeller som utgick fr ̊an typ 2 diabetes utfördes. Resultatet av studien visade en avsaknad av forskning gällande definieringen och kvantifieringen avhur man mäter åtlydnad för simuleringssyften. Ytterligare saknades det forskning rörande system med återkoppling från farmakologiska segment av ett system tillbaka till åtlydnadsdelarna. En enkel modell implementerades som ett förslag till hur en potentiell sammankoppling skulle kunna utföras. En känslighetsanalys utfördes och visade att poängskattningen för åtlydnad, SDSCA (Summary of Diabetes Self-Care Activities), hade en måttlig korrelation (Spearman ρ=−0.478∗∗∗) till den slutgiltiga koncentrationen av glukos i plasma. Detta resultat innebär att åtlydnad har en koppling till förbättrandet av hyperglykemi och bör därför inte exkluderas vid framtida utveckling av modeller för farmakologi. En länk skulle kunna vara kopplingen mellan ”åtlydnads-poäng” och doseringsregim.

Background: The sensitivity to drug-induced QT prolongation is highly variable in heart failure (HF) patients. QT interval prolongation can lead to a life-threatening ventricular arrhythmia known as torsade de Pointes (TdP), which can result in sudden cardiac death. Although QT prolongation is a surrogate marker for sudden cardiac death, the extent of drug-induced QT prolongation, and thus TdP, is largely unpredictable. Therefore, developing a biomarker to predict patients’ sensitivity to drug-induced QTc prolongation could have a profound clinical impact. MicroRNA (miR) are recognized as important regulators of cardiovascular function as they shape the transcriptome by targeting mRNAs for repression of translation. Our multidisciplinary research group has demonstrated that miR-362-3p regulates a potassium channel (i.e., hERG) that is the most widely implicated in drug-induced QTc prolongation. The primary objects of this analysis focus on characterizing serum miR-362-3p expression in the circulation as a potential biomarker to predict subject’s susceptibility to ibutilide exposure induced QT-interval prolongation.<div><br></div><div>Methods: The dataset utilized to develop the PK-PD models were collected from a previous clinical study carried out by Tisdale et al. (Tisdale,et al. 2020).A total of 22 adult subjects who met the inclusion and exclusion criteria were enrolled and divided into three groups: a group of patients with heart failure with preserved ejection fraction (HFpEF, n=10), a group of patients with heart failure with reduced ejection fraction (HFrEF, n=2), and ten healthy subjects in the control group who were matched to subjects in the HFpEF group for age and sex. Following a baseline day of triplicate 12-lead ECGs, all subjects received ibutilide 0.003mg/kg intravenously infused over 10 minutes. Serial collection of blood samples to determine serum Ibutilide concentrations (HPLC/MS), serum miR-362-3 expression (qPCR), with triplicate ECG readings were obtained pre-and-post ibutilide administration. To describe ibutilide serum concentration exposure and the9relationship with Fridericia-corrected QT (QTF) intervals, a non-linear mixed effect modeling approach was used along with clinical and demographic data, and serum miR-362-3p expression was evaluated as potential covariates on the PK/PD model.<div><br></div><div>Results: A three-compartment model best described the time course of ibutilide concentrations profile with a proportional residual error. The individual ibutilide concentrations time profile was then used in an indirect response model where ibutilide concentrations are indirectly driving the QT interval prolongation through inhibition of the output (Kout) parameters linked to an indirect response model with zero‐order input parameter best described the ibutilide concentrations QT interval lengthening relationship. The Individual PK/PD parameters using the base model for the Imax and IC50 were 11.4% (9.9%RES) and 0.36(8.4% RES)ng/mL, respectively. Following stepwise forwarding inclusion steps, the final covariate analyses identified circulating miR-362-3p expression associated with a history of myocardial infarction covariate influencing both the Imax and IC50( p<0.05). <div><br></div><div>Conclusions: An indirect response model has been developed to describe the effects of ibutilide concentrations on QT-intervals. Although the semi-mechanistic model could not be developed; serummiR-362-3p expression was identified as a significant predictor for ibutilide-induced QT-interval prolongation. Moreover, the upregulation of serum miR-362-3p expression enhanced IC50 seen after ibutilide administration. The potential use of miR-362-3p as a biomarker warrants further investigation to identify patients at the greatest risk of TdP </div></div></div>