Integrated environmental management and GPS-X modelling for current and future sustainable wastewater treatment: A case study from the Middle East.

In the context of today's rapidly changing environmental challenges, accurately predicting the performance and efficiency of environmental management strategies is crucial. Particularly in the Middle East, where research on wastewater treatment plants (WWTPs) is notably lacking, addressing this need is imperative. This study investigates the treatment efficiency of a wastewater treatment plant and proposes various techniques to enhance its performance. Employing a case study method, we utilise the GPS-X model to forecast the plant's performance under diverse scenarios, offering solutions for future challenges. The results reveal that the current plant layout operates efficiently, with removal efficiencies for Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) at 98.3 %, 95.1 %, and 96.1 %, respectively. The outlet Dissolved Oxygen (DO) of 1.9 mg/L meets local wastewater reuse standards. Furthermore, the GPS-X model forecasts the plant's performance under different scenarios, suggesting the feasibility of a new layout within 20-25 years and the need for additional units after 40 years. As inflow approaches maximum design capacity, simulation results underscore the importance of utilising the full plant design and expanding it for optimal operation over 60 years. This research provides critical insights for improving WWTP performance and emphasizes the significance of strategic planning in addressing long-term environmental management challenges. Moreover, this study represents a pioneering effort in addressing critical water scarcity challenges in Jordan by exploring the potential of treated wastewater (TWW) as a sustainable solution, thus contributing to the advancement of environmental management practices in the region.

Exposure-response modelling of osimertinib in patients with non-small cell lung cancer.

AIMS: Osimertinib is a third-generation, irreversible, central nervous system-active, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) with efficacy in EGFR-mutated non-small cell lung cancer (NSCLC). We assessed the relationship between plasma osimertinib levels and its efficacy and safety events. METHODS: Comprehensive pharmacokinetics exposure-response (E-R) modelling was performed utilizing steady state area under the curve (AUCss) data from first-line, ≥second-line and adjuvant studies from the osimertinib clinical development programme (20-240 mg once-daily dosing; N = 1689 patients). Analyses were conducted for survival using a proportional hazard model; for interstitial lung disease (ILD) and left ventricular ejection fraction (LVEF) events using a penalized logistic regression model and graphical analysis of potential confounding factors; and for rash and diarrhoea events using descriptive analysis. RESULTS: E-R modelling analyses indicated no clear trend of increasing efficacy with increasing osimertinib AUCss; efficacy in all exposure quartiles was significantly better than the control arm (comparator EGFR-TKI, chemotherapy or placebo) irrespective of treatment line. Model-based analysis suggested a potential relationship between increased osimertinib exposure and increased probability of ILD events, predominantly in Japanese patients. Additionally, there were increased probabilities of rash or diarrhoea with increasing osimertinib exposure. The probability of LVEF events showed overlapping confidence intervals for osimertinib ≤80 mg and control. CONCLUSIONS: E-R modelling in patients with EGFR-mutated NSCLC demonstrated that increased osimertinib exposure was unlikely to increase efficacy but may increase occurrence of certain adverse events. Hence, long-term treatment with doses ≥80 mg was not expected to provide additional benefit.

How take-off technique affects muscle demand in the back handspring step out in female gymnasts.

When performing the back handspring step out (BHS) on the balance beam, most gymnasts use one of three take-off techniques: Simultaneous Flexion, Sequential Flexion or Double-Bounce. However, it remains unclear which technique results in the lowest muscle demand that could help reduce energy expenditure and fatigue and improve overall performance. The purpose of this study was to use musculoskeletal modelling and simulation to quantify the influence of take-off technique on muscle demand (integrated muscle power) and contributions to the critical biomechanical functions of whole-body angular momentum generation and control and trunk propulsion (mechanical power delivered to the trunk). Simulations of female gymnasts (n = 21; age: 15.3 ± 3.6) were generated using their self-selected BHS technique on a balance beam. Differences in muscle demand were small across the techniques. However, the vasti, ankle plantarflexors, gluteus maximus and hamstring muscle groups experienced large demand during the BHS take-off. The gluteus medius and ankle plantarflexors were crucial for maintaining balance. The hamstrings, ankle plantarflexors and vasti generated needed momentum and delivered power to the trunk. These results provide targets for muscle strengthening and conditioning to improve balance control and increase the height and distance of the BHS, which is needed before adding additional skills in combination.

Modelling, simulation, and measurement of solar power generation: New developments in design and operational models.

The discrepancy between the operating and design capacities of solar plants in eastern Uganda is alarming; about 35 % underperformance in solar power generation is observed. The goal of the current study is to minimize this disparity by improving the design models. Considering only cell temperature in the power generation model is responsible for the observed difference in design and operational solar power generated, the present study used a thermocouple to directly measure cell temperature, an anemometer to measure wind speed, and a solar power meter to measure irradiance. These extrinsic factors were used to modify the power generation model based only on cell temperature through the direct correlation of cell temperature, wind speed, and irradiance with solar power generation. Thus, the absence of extrinsic factors (wind speed and irradiance) in the design models is responsible for the colossal drop in solar power generated. Empirically, the missing extrinsic factors were used to transform the implicit solar power model into an explicit model. The development of a solar power generation model, multiple differential models, simulation and experimentation with a pilot solar rig served as alternate model for the prediction of solar power generation. The second-order differential model validated well with empirical solar power generated in Busitema, Mayuge, Soroti, and Tororo study areas based on RMSEs (0.6437, 0.6692, 0.2008, 0.1804, respectively), thus, narrowing the gap between the designed and operational solar power generated. Mayuge and Soroti recorded the highest solar power generation of 9.028 MW compared to Busitema (8.622 MW) and Tororo (8.345 MW), suggesting that it has a conducive site for installing future solar plants. The above results support the use of empirical explicit (triple) and second-order differential models for the design and operation of power plants.

Population kinetic/pharmacodynamic modelling of the haemodynamic effects of cafedrine/theodrenaline (Akrinor) under general anaesthesia.

AIMS: The 20:1 combination of cafedrine and theodrenaline (C/T) is widely used in Germany for the treatment of arterial hypotension. Since there is little knowledge about the impact of covariates on the effect, the aim was to develop a kinetic/pharmacodynamic covariate model describing mean arterial pressure (MAP), systolic (SBP) and diastolic blood pressure (DBP), and heart rate (HR) for 30 min after the administration of C/T. METHODS: Data of patients receiving C/T from the HYPOTENS study (NCT02893241, DRKS00010740) were analysed using nonlinear mixed-effects modelling techniques. RESULTS: Overall, 16 579 measurements from 315 patients were analysed. The combination of two kinetic compartments and a delayed effect model, coupled with distinct Emax models for HR, SBP and DBP, described the data best. The model included age, sex, body mass index (BMI), antihypertensive medication, American Society of Anaesthesiologists (ASA) physical status classification grade, baseline SBP at the time of hypotension and pre-surgery HR as covariates (all P < .001). A higher baseline SBP led to a lower absolute increase in MAP. Patients with higher age, higher BMI and lower ASA grade showed smaller increases in MAP. The initial increase was similar for male and female patients. The long-term effect was higher in women. Concomitant antihypertensive medication caused a delayed effect and a lower maximum MAP. The HR increased only slightly (median increase 2.6 bpm, P < .001). CONCLUSIONS: Seven covariates with an impact on the effect of C/T could be identified. The results will enable physicians to optimize the dose with respect to individual patients.

Mechanisms of ischaemia-induced arrhythmias in hypertrophic cardiomyopathy: a large-scale computational study.

AIMS: Lethal arrhythmias in hypertrophic cardiomyopathy (HCM) are widely attributed to myocardial ischaemia and fibrosis. How these factors modulate arrhythmic risk remains largely unknown, especially as invasive mapping protocols are not routinely used in these patients. By leveraging multiscale digital twin technologies, we aim to investigate ischaemic mechanisms of increased arrhythmic risk in HCM. METHODS AND RESULTS: Computational models of human HCM cardiomyocytes, tissue, and ventricles were used to simulate outcomes of Phase 1A acute myocardial ischaemia. Cellular response predictions were validated with patch-clamp studies of human HCM cardiomyocytes (n = 12 cells, N = 5 patients). Ventricular simulations were informed by typical distributions of subendocardial/transmural ischaemia as analysed in perfusion scans (N = 28 patients). S1-S2 pacing protocols were used to quantify arrhythmic risk for scenarios in which regions of septal obstructive hypertrophy were affected by (i) ischaemia, (ii) ischaemia and impaired repolarization, and (iii) ischaemia, impaired repolarization, and diffuse fibrosis. HCM cardiomyocytes exhibited enhanced action potential and abnormal effective refractory period shortening to ischaemic insults. Analysis of ∼75 000 re-entry induction cases revealed that the abnormal HCM cellular response enabled establishment of arrhythmia at milder ischaemia than otherwise possible in healthy myocardium, due to larger refractoriness gradients that promoted conduction block. Arrhythmias were more easily sustained in transmural than subendocardial ischaemia. Mechanisms of ischaemia-fibrosis interaction were strongly electrophysiology dependent. Fibrosis enabled asymmetric re-entry patterns and break-up into sustained ventricular tachycardia. CONCLUSION: HCM ventricles exhibited an increased risk to non-sustained and sustained re-entry, largely dominated by an impaired cellular response and deleterious interactions with the diffuse fibrotic substrate.

Effects of ranolazine on the arrhythmic substrate in hypertrophic cardiomyopathy.

Introduction: Hypertrophic cardiomyopathy (HCM) is a leading cause of lethal arrhythmias in the young. Although the arrhythmic substrate has been hypothesised to be amenable to late Na+ block with ranolazine, the specific mechanisms are not fully understood. Therefore, this study aimed to investigate the substrate mechanisms of safety and antiarrhythmic efficacy of ranolazine in HCM. Methods: Computational models of human tissue and ventricles were used to simulate the electrophysiological behaviour of diseased HCM myocardium for variable degrees of repolarisation impairment, validated against in vitro and clinical recordings. S1-S2 pacing protocols were used to quantify arrhythmic risk in scenarios of (i) untreated HCM-remodelled myocardium and (ii) myocardium treated with 3µM, 6µM and 10µM ranolazine, for variable repolarisation heterogeneity sizes and pacing rates. ECGs were derived from biventricular simulations to identify ECG biomarkers linked to antiarrhythmic effects. Results: 10µM ranolazine given to models manifesting ventricular tachycardia (VT) at baseline led to a 40% reduction in number of VT episodes on pooled analysis of >40,000 re-entry inducibility simulations. Antiarrhythmic efficacy and safety were dependent on the degree of repolarisation impairment, with optimal benefit in models with maximum JTc interval <370 ms. Ranolazine increased risk of VT only in models with severe-extreme repolarisation impairment. Conclusion: Ranolazine efficacy and safety may be critically dependent upon the degree of repolarisation impairment in HCM. For moderate repolarisation impairment, reductions in refractoriness heterogeneity by ranolazine may prevent conduction blocks and re-entry. With severe-extreme disease substrates, reductions of the refractory period can increase re-entry sustainability.

Population pharmacokinetics of a novel PCSK9 antisense oligonucleotide.

AIMS: The aim of this study was to characterize the population pharmacokinetics of AZD8233, an antisense oligonucleotide (ASO) that targets the PCSK9 transcript to reduce hepatocyte PCSK9 protein production and plasma levels. AZD8233 utilizes generation 2.5 S-constrained ethyl motif (cET) chemistry and is conjugated to a triantennary N-acetylgalactosamine (GalNAc3) ligand for targeted hepatocyte uptake. METHODS: A non-linear mixed-effect modelling approach utilizing NONMEM software was applied to AZD8233 concentration-time data from 3416 samples in 219 participants from four phase 1-2 studies, one in healthy volunteers (NCT03593785) and three in patients with dyslipidaemia (NCT04155645, NCT04641299 and NCT04823611). RESULTS: The final model described the AZD8233 plasma concentration-time profile from four phase 1-2 studies in healthy volunteers or participants with dyslipidaemia, covering a dose range of 4 to 120 mg. The pharmacokinetics of AZD8233 were adequately described by a two-compartment model with first-order absorption. The supra-proportional increase in maximum plasma concentration (Cmax) across the observed dose range was described by non-linear Michaelis-Menten elimination (maximum elimination rate, 9.9 mg/h [12% relative standard error]; concentration yielding half-maximal elimination rate, 4.8 mg/L [18% relative standard error]). Body weight, sex, estimated glomerular filtration rate and disease status (healthy participant vs. patient with dyslipidaemia) were identified as factors affecting exposure to AZD8233. CONCLUSIONS: Covariate analysis showed body weight to be the main factor affecting exposure to AZD8233, which largely explained the higher Cmax observed in the Asian population relative to non-Asians.

Synthetic biological neural networks: From current implementations to future perspectives.

Artificial neural networks, inspired by the biological networks of the human brain, have become game-changing computing models in modern computer science. Inspired by their wide scope of applications, synthetic biology strives to create their biological counterparts, which we denote synthetic biological neural networks (SYNBIONNs). Their use in the fields of medicine, biosensors, biotechnology, and many more shows great potential and presents exciting possibilities. So far, many different synthetic biological networks have been successfully constructed, however, SYNBIONN implementations have been sparse. The latter are mostly based on neural networks pretrained in silico and being heavily dependent on extensive human input. In this paper, we review current implementations and models of SYNBIONNs. We briefly present the biological platforms that show potential for designing and constructing perceptrons and/or multilayer SYNBIONNs. We explore their future possibilities along with the challenges that must be overcome to successfully implement a scalable in vivo biological neural network capable of online learning.

Differential effects of mechano-electric feedback mechanisms on whole-heart activation, repolarization, and tension.

The human heart is subject to highly variable amounts of strain during day-to-day activities and needs to adapt to a wide range of physiological demands. This adaptation is driven by an autoregulatory loop that includes both electrical and the mechanical components. In particular, mechanical forces are known to feed back into the cardiac electrophysiology system, which can result in pro- and anti-arrhythmic effects. Despite the widespread use of computational modelling and simulation for cardiac electrophysiology research, the majority of in silico experiments ignore this mechano-electric feedback entirely due to the high computational cost associated with solving cardiac mechanics. In this study, we therefore use an electromechanically coupled whole-heart model to investigate the differential and combined effects of electromechanical feedback mechanisms with a focus on their physiological relevance during sinus rhythm. In particular, we consider troponin-bound calcium, the effect of deformation on the tissue diffusion tensor, and stretch-activated channels. We found that activation of the myocardium was only significantly affected when including deformation into the diffusion term of the monodomain equation. Repolarization, on the other hand, was influenced by both troponin-bound calcium and stretch-activated channels and resulted in steeper repolarization gradients in the atria. The latter also caused afterdepolarizations in the atria. Due to its central role for tension development, calcium bound to troponin affected stroke volume and pressure. In conclusion, we found that mechano-electric feedback changes activation and repolarization patterns throughout the heart during sinus rhythm and lead to a markedly more heterogeneous electrophysiological substrate. KEY POINTS: The electrophysiological and mechanical function of the heart are tightly interrelated by excitation-contraction coupling (ECC) in the forward direction and mechano-electric feedback (MEF) in the reverse direction. While ECC is considered in many state-of-the-art computational models of cardiac electromechanics, less is known about the effect of different MEF mechanisms. Accounting for calcium bound to troponin increases stroke volume and delays repolarization. Geometry-mediated MEF leads to more heterogeneous activation and repolarization with steeper gradients. Both effects combine in an additive way. Non-selective stretch-activated channels as an additional MEF mechanism lead to heterogeneous diastolic transmembrane voltage, higher developed tension and delayed repolarization or afterdepolarizations in highly stretched parts of the atria. The differential and combined effects of these three MEF mechanisms during sinus rhythm activation in a human four-chamber heart model may have implications for arrhythmogenesis, both in terms of substrate (repolarization gradients) and triggers (ectopy).

Decoupled optimal control of 3D biped for human voluntary motion.

The sit-to-stand (STS) model from a biomechanical point of view is an enormously important subject, with motor controls simulating human intended behavior. Physiological motion-based biomechanical research is important for designing whole-body prosthetics and understanding physical disabilities. The control strategies for biomechanical models can effectively synergize with the central nervous system (CNS) to facilitate the desired movements of individuals with neurological disabilities. In this study, we present our novel 3D biped model by decoupling it into healthy and neurologically deficient joints. The developed 8-segment model (i.e., 2× feet, 2× shanks, 2× thighs, 1× pelvic, and 1× Head Arm Torso (HAT) segment) with 10 joints is decoupled into 6 healthy joints and 4 deficient joints. This decoupling mimics stroke patients or subjects with neuromuscular deficiency. This novel decoupling establishes through asymmetrical torques in frontal and sagittal plane joints on a bipedal design with one foot fixed and the other a sliding tilt joint. In this design, two decoupled controllers collaborate to stabilize the nonlinear model for biped STS transfer. Utilizing the xml files from SOLIDWORKS, the model is linearized in SIMSCAPE / SIMULINK. We further imply the Linear Quadratic Regulator (LQR) optimal controller design in MATLAB / SIMULINK for torques in both the sagittal and frontal planes, respectively, for six healthy and four deficient joints. We also comprehend the forward thrust velocity controls to pragmatically model the STS of stroke patients. This decoupling enhanced the overall stability of the system and simulated more relevant angular and velocity profiles for neurologically deficient substances.

ChatGPT in pharmacometrics? Potential opportunities and limitations.

The potential of using ChatGPT in pharmacometrics was explored in this study, with a focus on developing a population pharmacokinetic (PK) model for standard half-life factor VIII. Our results demonstrated that ChatGPT can be utilized to accurately obtain typical PK parameters from literature, generate a population PK model in R and develop an interactive Shiny application to visualize the results. ChatGPT's language generation capabilities enabled the development of R codes with minimal programming knowledge and helped to identify as well fix errors in the code. While ChatGPT presents several advantages, such as its ability to streamline the development process, its use in pharmacometrics also has limitations and challenges, including the accuracy and reliability of AI-generated data, the lack of transparency and reproducibility regarding codes generated by ChatGPT. Overall, our study demonstrates the potential of using ChatGPT in pharmacometrics, but researchers must carefully evaluate its use for their specific needs.

Combined translational pharmacometrics approach to support the design and conduct of the first-in-human study of DWP16001.

AIMS: The objective of this study was to characterize the pharmacokinetics (PK)/pharmacodynamics (PD) of DWP16001, a novel sodium-glucose cotransporter 2 inhibitor, and predict efficacious doses for the first-in-human study using various translational approaches. METHODS: A mechanistic PK/PD model was developed for DWP16001 using nonlinear mixed-effect modelling to describe animal PK/PD properties. Using allometry and in silico physiologically based equations, human PK parameters were predicted. Human PD parameters were scaled by applying interspecies difference and in vitro drug-specific factors. Human parameters were refined using early clinical data. Model-predicted PK and PD outcomes were compared to observations before and after parameter refinement. RESULTS: The PK/PD model of DWP16001 was developed using a 2-compartment model with first-order absorption and indirect response. Efficacious doses of 0.3 and 2 mg of DWP16001 were predicted using human half-maximal inhibitory concentration values translated from Zucker Diabetic Fatty rats and normal rats, respectively. After parameter refinement, doses of 0.2 and 1 mg were predicted to be efficacious for each disease model, which improved the prediction results to within a 1.2-fold difference between the model prediction and observation. CONCLUSIONS: This study predicted efficacious human doses of DWP16001 using population PK/PD modelling and a combined translational pharmacometrics approach. Early clinical data allowed the methods used to translate in vitro and in vivo findings to clinical PK/PD values for DWP16001 to be optimized. This study has shown that a refinement step can be readily applied to improve model prediction and further support the study design and conduct of a first-in-human study.

Population pharmacokinetic analysis of doravirine in real-world people with HIV.

AIMS: The pharmacokinetics of doravirine has been studied in clinical trials but not in real-world settings. Our study aims to characterize and identify factors influencing doravirine (a CYP3A4 substrate) pharmacokinetics in real-world people with HIV (PWH). METHODS: A total of 174 doravirine concentrations measured in 146 PWH followed up in the therapeutic drug monitoring (TDM) program at the University Hospital of Lausanne (Switzerland) between 2019 and 2023 were included in the analysis. Demographic data, clinical information and comedications were recorded during the routine SHCS visits (every 3-6 months). Population pharmacokinetic analysis and Monte Carlo simulations to investigate the clinical significance of the covariates retained in the final model were performed using NONMEM. RESULTS: A one-compartment model with first-order absorption and linear elimination best described doravirine pharmacokinetics. Potent CYP3A4 inhibitors and, to a lesser extent age, were the only tested covariates to significantly impact doravirine clearance (CL). Potent CYP3A4 inhibitors reduced CL by 50%, and a 30% decrease in CL was observed in an 80-year-old compared with a 55-year-old PWH. The effect of potent CYP3A4 inhibitors was prominent, explaining 59% of between-subject variability in CL. Model-based simulations predicted 2.8-fold and 1.6-fold increases in median steady-state trough and maximum doravirine concentrations, respectively, when a potent CYP3A4 inhibitor was co-administered. CONCLUSIONS: Our findings show that potent CYP3A4 inhibitors and age influence doravirine pharmacokinetics. However, given the good tolerability of doravirine, dosing adjustment of doravirine is probably not mandatory in those situations. TDM remains useful essentially in specific clinical situations, such as hepatic impairment, suspected nonadherence or pregnancy.

Unpeeling the onion: Digital triage and monitoring of general practice, private psychiatry, and psychology.

OBJECTIVE: The Australian federal government is considering a 'digital front door' to mental healthcare. The Brain and Mind Centre at the University of Sydney has published a discussion paper advocating that the government should adopt a comprehensive model of digital triage and monitoring (DTM) based on a government-funded initiative Project Synergy ($30 million). We critically examine the final report on Project Synergy, which is now available under a Freedom of Information request. CONCLUSION: The DTM model is disruptive. Non-government organisations would replace general practitioners as care coordinators. Patients, private psychiatrists, and psychologists would be subjected to additional layers of administration, assessment, and digital compliance, which may decrease efficiency, and lengthen the duration of untreated illness. Only one patient was deemed eligible for DTM, however, during the 8-month regional trial of Project Synergy (recruitment rate = 1/500,000 across the region). Instead of an unproven DTM model, the proposed 'digital front door' to Australian mental healthcare should emphasise technology-enabled shared care (general practitioners and mental health professionals) for the treatment of moderate-to-severe illness.

Prediction of pharmacokinetics of an anaplastic lymphoma kinase inhibitor in rat and monkey: application of physiologically based pharmacokinetic model as an alternative tool to minimise animal studies.

The pharmacokinetic (PK) and toxicokinetic profile of a drug from its preclinical evaluation helps the researcher determine whether the drug should be tested in humans based on its safety and toxicity.Preclinical studies require time and resources and are prone to error. Moreover, according to the United States Food and Drug Administration Modernisation Act 2, animal testing is no longer mandatory for new drug development, and an animal-free alternative, such as cell-based assay and computer models, can be used.Different physiologically based PK models were developed for an anaplastic lymphoma kinase inhibitor in rats and monkeys after intravenous and oral administration using its physicochemical properties and in vitro characterisation data.The developed model was validated against the in vivo data available in the literature, and the validation results were found within the acceptable limit. A parameter sensitivity analysis was performed to identify the properties of the compound influencing the PK profile.This work demonstrates the application of the physiologically based PK model to predict the PKs of a drug, which will eventually assist in reducing the number of animal studies and save time and cost of drug discovery and development.

A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers.

The muscle spindle is an essential proprioceptor, significantly involved in sensing limb position and movement. Although biological spindle models exist for years, the gold-standard for motor control in biomechanics are still sensors built of homogenized spindle output models due to their simpler combination with neuro-musculoskeletal models. Aiming to improve biomechanical simulations, this work establishes a more physiological model of the muscle spindle, aligned to the advantage of easy integration into large-scale musculoskeletal models. We implemented four variations of a spindle model in Matlab/Simulink®: the Mileusnic et al. (2006) model, Mileusnic model without mass, our enhanced Hill-type model, and our enhanced Hill-type model with parallel damping element (PDE). Different stretches in the intrafusal fibers were simulated in all model variations following the spindle afferent recorded in previous experiments in feline soleus muscle. Additionally, the enhanced Hill-type models had their parameters extensively optimized to match the experimental conditions, and the resulting model was validated against data from rats' triceps surae muscle. As result, the Mileusnic models present a better overall performance generating the afferent firings compared to the common data evaluated. However, the enhanced Hill-type model with PDE exhibits a more stable performance than the original Mileusnic model, at the same time that presents a well-tuned Hill-type model as muscle spindle fibers, and also accounts for real sarcomere force-length and force-velocity aspects. Finally, our activation dynamics is similar to the one applied to Hill-type model for extrafusal fibers, making our proposed model more easily integrated in multi-body simulations.

Challenges Related to Acquisition of Physiological Data for Physiologically Based Pharmacokinetic (PBPK) Models in Postpartum, Lactating Women and Breastfed Infants-A Contribution from the ConcePTION Project.

Physiologically based pharmacokinetic (PBPK) modelling is a bottom-up approach to predict pharmacokinetics in specific populations based on population-specific and medicine-specific data. Using an illustrative approach, this review aims to highlight the challenges of incorporating physiological data to develop postpartum, lactating women and breastfed infant PBPK models. For instance, most women retain pregnancy weight during the postpartum period, especially after excessive gestational weight gain, while breastfeeding might be associated with lower postpartum weight retention and long-term weight control. Based on a structured search, an equation for human milk intake reported the maximum intake of 153 mL/kg/day in exclusively breastfed infants at 20 days, which correlates with a high risk for medicine reactions at 2-4 weeks in breastfed infants. Furthermore, the changing composition of human milk and its enzymatic activities could affect pharmacokinetics in breastfed infants. Growth in breastfed infants is slower and gastric emptying faster than in formula-fed infants, while a slower maturation of specific metabolizing enzymes in breastfed infants has been described. The currently available PBPK models for these populations lack structured systematic acquisition of population-specific data. Future directions include systematic searches to fully identify physiological data. Following data integration as mathematical equations, this holds the promise to improve postpartum, lactation and infant PBPK models.

A Comparative Evaluation of Desoximetasone Cream and Ointment Formulations Using Experiments and In Silico Modeling.

The administration of therapeutic drugs through dermal routes, such as creams and ointments, has emerged as an increasingly popular alternative to traditional delivery methods, such as tablets and injections. In the context of drug development, it is crucial to identify the optimal doses and delivery routes that ensure successful outcomes. Physiologically based pharmacokinetic (PBPK) models have been proposed to simulate drug delivery and optimize drug formulations, but the calibration of these models is challenging due to the multitude of variables involved and limited experimental data. One significant research gap that this article addresses is the need for more efficient and accurate methods for calibrating PBPK models for dermal drug delivery. This manuscript presents a novel approach and an integrated dermal drug delivery model to address this gap that leverages virtual in vitro release (IVRT) and permeation (IVPT) testing data to optimize mechanistic models. The proposed approach was demonstrated through a study involving Desoximetasone cream and ointment formulations, where the release kinetics and permeation profiles of Desoximetasone were determined experimentally, and a computational model was created to simulate the results. The experimental studies showed that, even though the cumulative permeation of Desoximetasone at the end of the permeation study was comparable, there was a significant difference seen in the lag time in the permeation of Desoximetasone between the cream and ointment. Additionally, there was a significant difference seen in the amount of Desoximetasone permeated through human cadaver skin at early time points when the cream and ointment were compared. The computational model was optimized and validated, suggesting that this approach has the potential to bridge the existing research gap by improving the accuracy and efficiency of drug development processes. The model results show a good fit between the experimental data and model predictions. During the model optimization process, it became evident that there was variability in both the permeability and the partition coefficient within the stratum corneum. This variability had a significant and noteworthy influence on the overall performance of the model, especially when it came to its capacity to differentiate between cream and ointment formulations. Leveraging virtual models significantly aids the comprehension of drug release and permeation, mitigating the demanding data requirements. The use of virtual IVRT and IVPT data can accelerate the calibration of PBPK models, streamline the selection of the appropriate doses, and optimize drug delivery. Moreover, this novel approach could potentially reduce the time and resources involved in drug development, thus making it more cost-effective and efficient.