Exploring the thermally-controlled fentanyl transdermal therapy to provide constant drug delivery by physics-based digital twins.

Transdermal drug delivery is suitable for low-molecular-weight drugs with specific lipophilicity, like fentanyl, which is widely used for cancer-induced pain management. However, fentanyl's transdermal therapy displays high intra-individual variability. Factors like skin characteristics at application sites and ambient temperature contribute to this variation. In this study, we developed a physics-based digital twin of the human body to cope with this variability and propose better adapted setups. This twin includes an in-silico skin model for drug penetration, a pharmacokinetic model, and a pharmacodynamic model. Based on the results of our simulations, applying the patch on the flank (side abdominal area) showed a 15.3% higher maximum fentanyl concentration in the plasma than on the chest. Additionally, the time to reach this maximum concentration when delivered through the flank was 19.8 h, which was 10.3 h earlier than via the upper arm. Finally, this variation led to an 18% lower minimum pain intensity for delivery via the flank than the chest. Moreover, the impact of seasonal changes on ambient temperature and skin temperature by considering the activity level was investigated. Based on our result, the fentanyl uptake flux by capillaries increased by up to 11.8% from an inactive state in winter to an active state in summer. We also evaluated the effect of controlling fentanyl delivery by adjusting the temperature of the patch to alleviate the pain to reach a mild pain intensity (rated three on the VAS scale). By implementing this strategy, the average pain intensity decreased by 1.1 points, and the standard deviation for fentanyl concentration in plasma and average pain intensity reduced by 37.5% and 33.3%, respectively. Therefore, our digital twin demonstrated the efficacy of controlled drug release through temperature regulation, ensuring the therapy toward the intended target outcome and reducing therapy outcome variability. This holds promise as a potentially useful tool for physicians.

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.

An individualized digital twin of a patient for transdermal fentanyl therapy for chronic pain management.

Fentanyl transdermal therapy is a suitable treatment for moderate-to-severe cancer-related pain. The inter-individual variability of the patients leads to different therapy responses. This study aims to determine the effect of physiological features on the achieved pain relief. Therefore, a set of virtual patients was developed by using Markov chain Monte Carlo (MCMC) based on actual patient data. The members of this virtual population differ by age, weight, gender, and height. Tailored digital twins were developed using these correlated, individualized parameters to propose a personalized therapy for each patient. It was shown that patients of different ages, weights, and gender have significantly different fentanyl blood uptake, plasma fentanyl concentration, pain relief, and ventilation rate. In the digital twins, we included the virtual patients' response to the treatment, namely, pain relief. Therefore, the digital twin was able to adjust the therapy in silico to have more efficient pain relief. By implementing digital-twin-assisted therapy, the average pain intensity decreased by 16% compared to conventional therapy. The median time without pain increased by 23 h over 72 h. Therefore, the digital twin can be successfully used in individual control of transdermal therapy to reach higher pain relief and maintain steady pain relief. (Created with BioRender.com).

How long and effective does a mask protect you from an infected person who emits virus-laden particles: By implementing one-dimensional physics-based modeling.

SARS-CoV-2 spreads via droplets, aerosols, and smear infection. From the beginning of the COVID-19 pandemic, using a facemask in different locations was recommended to slow down the spread of the virus. To evaluate facemasks' performance, masks' filtration efficiency is tested for a range of particle sizes. Although such tests quantify the blockage of the mask for a range of particle sizes, the test does not quantify the cumulative amount of virus-laden particles inhaled or exhaled by its wearer. In this study, we quantify the accumulated viruses that the healthy person inhales as a function of time, activity level, type of mask, and room condition using a physics-based model. We considered different types of masks, such as surgical masks and filtering facepieces (FFPs), and different characteristics of public places such as office rooms, buses, trains, and airplanes. To do such quantification, we implemented a physics-based model of the mask. Our results confirm the importance of both people wearing a mask compared to when only one wears the mask. The protection time for light activity in an office room decreases from 7.8 to 1.4 h with surgical mask IIR. The protection time is further reduced by 85 and 99% if the infected person starts to cough or increases the activity level, respectively. Results show the leakage of the mask can considerably affect the performance of the mask. For the surgical mask, the apparent filtration efficiency reduces by 75% with such a leakage, which cannot provide sufficient protection despite the high filtration efficiency of the mask. The facemask model presented provides key input in order to evaluate the protection of masks for different conditions in public places. The physics-based model of the facemask is provided as an online application.

Predicting transdermal fentanyl delivery using physics-based simulations for tailored therapy based on the age.

Transdermal fentanyl patches are an effective alternative to the sustained release of oral morphine for chronic pain management. Due to the narrow therapeutic range of fentanyl, the concentration of fentanyl in the blood needs to be carefully monitored. Only then can effective pain relief be achieved while avoiding adverse effects such as respiratory depression. This study developed a physics-based digital twin of a patient by implementing drug uptake, pharmacokinetics, and pharmacodynamics models. The twin was employed to predict the in-silico effect of conventional fentanyl transdermal in a 20-80-year-old virtual patient. The results show that, with increasing age, the maximum transdermal fentanyl flux and maximum concentration of fentanyl in the blood decreased by 11.4% and 7.0%, respectively. However, the results also show that as the patient's age increases, the pain relief increases by 45.2%. Furthermore, the digital twin was used to propose a tailored therapy based on the patient's age. This predesigned therapy customized the duration of applying the commercialized fentanyl patches. According to this therapy, a 20-year-old patient needs to change the patch 2.1 times more frequently than conventional therapy, which leads to 30% more pain relief and 315% more time without pain. In addition, the digital twin was updated by the patient's pain intensity feedback. Such therapy increased the patient's breathing rate while providing effective pain relief, so a safer treatment. We quantified the added value of a patient's physics-based digital twin and sketched the future roadmap for implementing such twin-assisted treatment into the clinics.

Inverse Mechanistic Modeling of Transdermal Drug Delivery for Fast Identification of Optimal Model Parameters.

Transdermal drug delivery systems are a key technology to administer drugs with a high first-pass effect in a non-invasive and controlled way. Physics-based modeling and simulation are on their way to become a cornerstone in the engineering of these healthcare devices since it provides a unique complementarity to experimental data and additional insights. Simulations enable to virtually probe the drug transport inside the skin at each point in time and space. However, the tedious experimental or numerical determination of material properties currently forms a bottleneck in the modeling workflow. We show that multiparameter inverse modeling to determine the drug diffusion and partition coefficients is a fast and reliable alternative. We demonstrate this strategy for transdermal delivery of fentanyl. We found that inverse modeling reduced the normalized root mean square deviation of the measured drug uptake flux from 26 to 9%, when compared to the experimental measurement of all skin properties. We found that this improved agreement with experiments was only possible if the diffusion in the reservoir holding the drug was smaller than the experimentally measured diffusion coefficients suggested. For indirect inverse modeling, which systematically explores the entire parametric space, 30,000 simulations were required. By relying on direct inverse modeling, we reduced the number of simulations to be performed to only 300, so a factor 100 difference. The modeling approach's added value is that it can be calibrated once in-silico for all model parameters simultaneously by solely relying on a single measurement of the drug uptake flux evolution over time. We showed that this calibrated model could accurately be used to simulate transdermal patches with other drug doses. We showed that inverse modeling is a fast way to build up an accurate mechanistic model for drug delivery. This strategy opens the door to clinically ready therapy that is tailored to patients.

Predicting Transdermal Fentanyl Delivery Using Mechanistic Simulations for Tailored Therapy.

Transdermal drug delivery is a key technology for administering drugs. However, most devices are "one-size-fits-all", even though drug diffusion through the skin varies significantly from person-to-person. For next-generation devices, personalization for optimal drug release would benefit from an augmented insight into the drug release and percutaneous uptake kinetics. Our objective was to quantify the changes in transdermal fentanyl uptake with regards to the patient's age and the anatomical location where the patch was placed. We also explored to which extent the drug flux from the patch could be altered by miniaturizing the contact surface area of the patch reservoir with the skin. To this end, we used validated mechanistic modeling of fentanyl diffusion, storage, and partitioning in the epidermis to quantify drug release from the patch and the uptake within the skin. A superior spatiotemporal resolution compared to experimental methods enabled in-silico identification of peak concentrations and fluxes, and the amount of stored drug and bioavailability. The patients' drug uptake showed a 36% difference between different anatomical locations after 72 h, but there was a strong interpatient variability. With aging, the drug uptake from the transdermal patch became slower and less potent. A 70-year-old patient received 26% less drug over the 72-h application period, compared to an 18-year-old patient. Additionally, a novel concept of using micron-sized drug reservoirs was explored in silico. These reservoirs induced a much higher local flux (µg cm-2 h-1) than conventional patches. Up to a 200-fold increase in the drug flux was obtained from these small reservoirs. This effect was mainly caused by transverse diffusion in the stratum corneum, which is not relevant for much larger conventional patches. These micron-sized drug reservoirs open new ways to individualize reservoir design and thus transdermal therapy. Such computer-aided engineering tools also have great potential for in-silico design and precise control of drug delivery systems. Here, the validated mechanistic models can serve as a key building block for developing digital twins for transdermal drug delivery systems.