Implementing physics-based digital patient twins to tailor the switch of oral morphine to transdermal fentanyl patches based on patient physiology

Fentanyl transdermal patches are widely implemented for cancer-induced pain treatment due to the high potency of fentanyl and gradual drug release. However, transdermal fentanyl up-titration for opioid-naïve patients is difficult, which is why opioid treatment is often started with oral/iv morphine. Based on the daily dose of morphine, the initial dose of the fentanyl patch is decided upon. After reaching a stable level of pain, the switch is made from oral/iv morphine to transdermal fentanyl. There are standard calculation tools for transferring from oral/iv morphine to transdermal fentanyl, which is the same for all patients. By considering the variations in the physiology of the patients, a unique switching strategy cannot meet the needs of different patients. This study explores the outcome in terms of pain relief and minute ventilation during opioid therapy. For this, we used physics-based simulations on a virtually-generated population of patients, and we applied the same therapy to all patients. We could show that patients' physiology, such as gender, age, and weight, greatly impact the outcome of the therapy; as such, the correlation coefficient between pain intensity and age is 0.89, and the correlation coefficient between patient's weight and maximum plasma concentration of morphine and fentanyl is -0.98 and -0.97. Additionally, a different combination of the duration of overlap between morphine and fentanyl therapy with different doses of fentanyl was considered for the virtual patients to find the best opioid-switching strategy for each patient. We explored the impact of combining physiological features to determine the best-suited strategy for virtual patients. Our findings suggest that tailoring morphine and fentanyl therapy only based on a limited number of features is insufficient, and increasing the number of impactful physiological features positively influences the outcome of the therapy.

Virtual clinical QT exposure-response studies - A translational computational approach

BACKGROUND AND PURPOSE

A recent paradigm shift in proarrhythmic risk assessment suggests that the integration of clinical, non-clinical, and computational evidence can be used to reach a comprehensive understanding of the proarrhythmic potential of drug candidates. While current computational methodologies focus on predicting the incidence of proarrhythmic events after drug administration, the objective of this study is to predict concentration-response relationships of QTc as a clinical endpoint.

EXPERIMENTAL APPROACH

Full heart computational models reproducing human cardiac populations were created to predict the concentration-response relationship of changes in the QT interval as recommended for clinical trials. The concentration-response relationship of the QT-interval prolongation obtained from the computational cardiac population was compared against the relationship from clinical trial data for a set of well-characterized compounds: moxifloxacin, dofetilide, verapamil, and ondansetron.

KEY RESULTS

Computationally derived concentration-response relationships of QT interval changes for three of the four drugs had slopes within the confidence interval of clinical trials (dofetilide, moxifloxacin and verapamil) when compared to placebo-corrected concentration-ΔQT and concentration-ΔQT regressions. Moxifloxacin showed a higher intercept, outside the confidence interval of the clinical data, demonstrating that in this example, the standard linear regression does not appropriately capture the concentration-response results at very low concentrations. The concentrations corresponding to a mean QTc prolongation of 10 ms were consistently lower in the computational model than in clinical data. The critical concentration varied within an approximate ratio of 0.5 (moxifloxacin and ondansetron) and 1 times (dofetilide, verapamil) the critical concentration observed in human clinical trials. Notably, no other in silico methodology can approximate the human critical concentration values for a QT interval prolongation of 10 ms.

CONCLUSION AND IMPLICATIONS

Computational concentration-response modelling of a virtual population of high-resolution, 3-dimensional cardiac models can provide comparable information to clinical data and could be used to complement pre-clinical and clinical safety packages. It provides access to an unlimited exposure range to support trial design and can improve the understanding of pre-clinical-clinical translation.

Effect of Muscle Forces on Femur During Level Walking Using a Virtual Population of Older Women

Aging is associated with a greater risk of muscle and bone disorders such as sarcopenia and osteoporosis. These conditions substantially affect one's mobility and quality of life. In the past, muscles and bones are often studied separately using generic or scaled information that are not personal-specific, nor are they representative of the large variations seen in the elderly population. Consequently, the mechanical interaction between the aged muscle and bone is not well understood, especially when carrying out daily activities. This study presents a coupling approach across the body and the organ level, using fully personal-specific musculoskeletal and finite element models in order to study femoral loading during level walking. Variations in lower limb muscle volume/force were examined using a virtual population. These muscle forces were then applied to the finite element model of the femur to study the variations in predicted strains. The study shows that effective coupling across two scales can be carried out to study the muscle-bone interaction in elderly women. The generation of a virtual population is a feasible approach to augment anatomical variations based on a small population that could mimic variations seen in a larger cohort. This is a valuable alternative to overcome the limitation or the need to collect dataset from a large population, which is both time and resource consuming.

Variability and uncertainty: interpretation and usage of pharmacometric simulations and intervals

Variability and estimation uncertainty are important sources of variation in pharmacometric simulations. Different combinations of uncertainty and the variability components lead to a variety types of simulation intervals, and many realized and unrealized confusions exist among pharmacometricians on their interpretation and usage. This commentary aims to clarify some of the important underlying concepts and provide a convenient guideline on pharmacometric simulation conduct and interpretation.

Virtual Populations for Quantitative Systems Pharmacology Models

Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.

Detecting crucial dispersal pathways using a virtual ecology approach: A case study of the mirid bug Stenotus rubrovittatus

Detecting dispersal pathways is important both for understanding species range expansion and for managing nuisance species. However, direct detection is difficult. Here, we propose detecting these crucial pathways using a virtual ecology approach, simulating species dynamics using models, and virtual observations. As a case study, we developed a dispersal model based on cellular automata for the pest insect Stenotus rubrovittatus and simulated its expansion. We tested models for species expansion based on four landscape parameters as candidate pathways; these are river density, road density, area of paddy fields, and area of abandoned farmland, and validated their accuracy. We found that both road density and abandoned area models had prediction accuracy. The simulation requires simple data only to have predictive power, allowing for fast modeling and swift establishment of management plans.

Application of a physiologically based pharmacokinetic model for the evaluation of single-point plasma phenotyping method of CYP2D6

PURPOSE

Determining metabolic ratio from single-point plasma is potentially a good phenotyping method of CYP2D6 to reduce the required time interval and increase the reliability of data. It is difficult to conduct large sample size clinical trials to evaluate this phenotyping method for multiple plasma points. A physiologically based pharmacokinetic (PBPK) model can be developed to do simulations based on the large virtual Chinese population and evaluate single-point plasma phenotyping method of CYP2D6.

METHODS

Pharmacokinetic data of dextromethorphan (DM) and its metabolite dextrorphan (DX) after oral administration were used for model development. The SimCYP® model incorporating Chinese demographic, physiological, and enzyme data was used to simulate DM and DX pharmacokinetics in different phenotype groups.

RESULTS

The ratios of the simulated to the observed mean AUC and Cmax of DM were 1.01 and 0.81 for extensive metabolizers (EMs), 0.90 and 0.81 for intermediate metabolizers (IMs), and 1.12 and 0.84 for poor metabolizers (PMs). The ratios of the simulated to the observed mean AUC and Cmax of DX were 1.12 and 0.89 for EMs, 0.66 and 0.62 for IMs. All ratios were within the predefined criterion of 0.5-2. The simulations of DM and DX pharmacokinetic profiles in 1000 virtual Chinese subjects with reported frequencies of different phenotypes indicated that statistically significant correlations were found between metabolic ratio of DM to DX (MRDM/DX) from AUC and from single-point plasma from 1 to 30h (all p-values <0.001).

CONCLUSION

MRDM/DX from single-point plasma from 1 to 30h after the administration of 30mg controlled-release DM could predict the MRDM/DX from AUC well and could be used as the phenotyping method of CYP2D6 for EMs, IMs, and PMs.