Middle-out Approach Research Articles

Identifying transdiagnostic and multidimensional prognostic indicators among veterans with PTSD symptoms in brief integrated care settings.

Brief integrated care settings hold promise for accessible and effective trauma-informed interventions. However, clinicians often have difficulty efficiently forecasting who is most appropriate for interventions in such settings and how to target individualized care. Multidimensional and transdiagnostic evaluations may provide valuable information to improve stepped-care and treatment practices for veterans. A middle-out approach was used to concurrently evaluate self-reported posttraumatic stress disorder (PTSD) symptoms, depressive symptoms, and physical health problems using cross-sectional (latent profile analysis) and longitudinal (latent growth mixture modeling) analyses that identified unique symptom profiles, trajectories of traumatic stress reactions, and correlates of these health outcomes. Data from 234 veterans who participated in a randomized controlled trial of primary care PTSD intervention were analyzed at 0, 8, 16, and 24 weeks. Latent profile analysis identified two homogenous baseline profiles: global symptoms (33.8%); low dysphoria-lower negative cognitions, mood, and depressive symptoms (66.2%). Low dysphoria participants reported more social relationships (OR = 1.32) and fewer environmental (OR = 0.89) and financial (OR = 0.23-0.35) stressors. Latent growth mixture modeling identified three trajectories: (a) reducing symptoms ("responders"; 21.3%) and chronic symptoms of (b) moderate (59.6%) and (c) high (19.1%) severity. Low dysphoria participants were 4.35 times more likely to be responders over time compared to participants with moderate severity symptoms. Findings indicated that veterans with moderate PTSD symptoms and physical health problems but low dysphoria may respond best to trauma-informed intervention in brief integrated care settings, while others may require further individualized stepped care. Findings demonstrate unique traumatic stress reactions that support individualized stepped care and may offer greater treatment utilization, retention, and efficacy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Understanding Voriconazole Metabolism: A Middle-Out Physiologically-Based Pharmacokinetic Modelling Framework Integrating In Vitro and Clinical Insights.

Voriconazole (VRC), a broad-spectrum antifungal drug, exhibits nonlinear pharmacokinetics (PK) due to saturable metabolic processes, autoinhibition and metabolite-mediated inhibition on their own formation. VRC PK is also characterised by high inter- and intraindividual variability, primarily associated with cytochrome P450 (CYP)2C19 genetic polymorphism. Additionally, recent in vitro findings indicate that VRC main metabolites, voriconazole N-oxide (NO) and hydroxyvoriconazole (OHVRC), inhibit CYP enzymes responsible for VRC metabolism, adding to its PK variability. This variability poses a significant risk of therapeutic failure or adverse events, which are major challenges in VRC therapy. Understanding the underlying processes and sources of these variabilities is essentialfor safe and effective therapy. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) modelling framework that elucidates the complex metabolism of VRC and the impact of its metabolites, NO and OHVRC, on the PK of the parent, leveraging both in vitro and in vivo clinical data in a middle-out approach. A coupled parent-metabolite PBPK model for VRC, NO and OHVRC was developed in a stepwise manner using PK-Sim® and MoBi®. Based on available in vitro data, NO formation was assumed to be mediated by CYP2C19, CYP3A4, and CYP2C9, while OHVRC formation was attributed solely to CYP3A4. Both metabolites were assumed to be excreted via renal clearance, with hepatic elimination also considered for NO. Inhibition functions were implemented to describe the complex interaction network of VRC autoinhibition and metabolite-mediated inhibition on each CYP enzyme. Using a combined bottom-up and middle-out approach, incorporating data from multiple clinical studies and existing literature, the model accurately predicted plasma concentration-time profiles across various intravenous dosing regimens in healthy adults, of different CYP2C19 genotype-predicted phenotypes. All (100%) of the predicted area under the concentration-time curve (AUC) and 94% of maximum concentration (Cmax) values of VRC met the 1.25-fold acceptance criterion, with overall absolute average fold errors of 1.12 and 1.14, respectively. Furthermore, all predicted AUC and Cmax values of NO and OHVRC met the twofold acceptance criterion. This comprehensive parent-metabolite PBPK model of VRC quantitatively elucidated the complex metabolism of the drug and emphasised the substantial impact of the primary metabolites on VRC PK. The comprehensive approachcombining bottom-up and middle-out modelling, therebyaccounting for VRC autoinhibition, metabolite-mediated inhibition, and the impact of CYP2C19 genetic polymorphisms, enhances our understanding of VRC PK. Moreover, the model can be pivotal in designing further in vitro experiments, ultimately allowing for extrapolation to paediatric populations, enhance treatment individualisation and improve clinical outcomes.

Applications of the Cholesterol Metabolite, 4β-Hydroxycholesterol, as a Sensitive Endogenous Biomarker for Hepatic CYP3A Activity Evaluated within a PBPK Framework

Background/Objectives: Plasma levels of 4β-hydroxycholesterol (4β-OHC), a CYP3A-specific metabolite of cholesterol, are elevated after administration of CYP3A inducers like rifampicin and carbamazepine. To simulate such plasma 4β-OHC increase, we developed a physiologically based pharmacokinetic (PBPK) model of cholesterol and 4β-OHC in the Simcyp PBPK Simulator (Version 23, Certara UK Ltd.) using a middle-out approach. Methods: Relevant physicochemical properties and metabolic pathway data for CYP3A and CYP27A1 was incorporated in the model. Results: The PBPK model recovered the observed baseline plasma 4β-OHC levels in Caucasian, Japanese, and Korean populations. The model also captured the higher baseline 4β-OHC levels in females compared to males, indicative of sex-specific differences in CYP3A abundance. More importantly, the model recapitulated the increased 4β-OHC plasma levels after multiple-dose rifampicin treatment in six independent studies, indicative of hepatic CYP3A induction. The verified model also captured the altered 4β-OHC levels in CYP3A4/5 polymorphic populations and with other CYP3A inducers. The model is limited by scant data on relative contributions of CYP3A and CYP27A1 pathways and does not account for regulatory mechanisms that control plasma cholesterol and 4β-OHC levels. Conclusion: This study provides a quantitative fit-for-purpose and framed-for-future modelling framework for an endogenous biomarker to evaluate the DDI risk with hepatic CYP3A induction.

The Middle-Out Approach to reconceptualizing, assessing, and analyzing traumatic stress reactions.

Alternative models of traumatic stress and broader psychopathology have been proposed to address issues of heterogeneity, comorbidity, clinical utility, and equitable representation. However, systematic and practical methods and guidelines to organize and apply these models remain scarce. The Middle-Out Approach is a novel, integrative, contextually informed framework for organizing and applying existing empirical methods to evaluate current and alternative traumatic stress reactions. Rather than beginning to identify traumatic stress reactions from the top-down (i.e., disorder-first approach) or bottom-up (i.e., symptom-first approach), constructs are evaluated from the middle out (i.e., presentation-first approach), unconstrained by higher-order disorders or lower-order diagnostic symptoms. This approach provides innovation over previous methods at multiple levels, including the conceptualization of traumatic stress reactions as well as the type of assessments and data sources used and how they are used in statistical analyses. Conceptualizations prioritize the identification of middle-order phenotypes, representing person-centered clinical presentations, which are informed by the integration of multidimensional, transdiagnostic, and multimodal (e.g., psychosocial, physiological) assessments and/or data sources. Integrated data are then analyzed concurrently using person-centered statistical models to identify precise, discrete, and representative health outcomes within broader heterogeneous samples. Subsequent variable-centered analyses are then used to identify culturally sensitive and contextually informed correlates of phenotypes, their clinical utility, and the differential composition within and between broader traumatic stress reactions. Examples from the moral injury literature are used to illustrate practical applications that may increase clinical utility and the accurate representation of health outcomes for diverse individuals and communities.

Abstract A140: A physiologically based pharmacokinetic modeling approach for predicting the exposure of irinotecan and its active metabolite (SN-38) in cancer patients

Abstract Introduction: Irinotecan is an inhibitor of the topoisomerase I enzyme that is essential in DNA transcription, replication, and repair. It is considered a “backbone” regime in colorectal cancer in adults and in pediatric gliomas to which other antineoplastic agents are added to improve the outcome. It is used as well in the treatment of many other types of adult and pediatric cancers. Irinotecan acts as a prodrug of SN-38 which has approximately 100-1000-fold greater cytotoxic activity. The metabolic and pharmacokinetic behavior of irinotecan is very complex and currently not completely elucidated. Clinical data have shown high variability in treated patients in the efficacy, toxicity, and pharmacokinetic profile of the drug. To increase safety and efficacy, a physiologically based pharmacokinetic modeling (PBPK) approach was used to predict the optimal dose for treatment initiation. Methods: Clinical data were used in combination with measured physicochemical and blood-binding data to inform the model. Hence a middle-out approach was used to build the parent model for a population of adult cancer patients. While the default Sim-Cancer population available within the Simcyp Simulator (v21) was used, the unbound fraction in plasma for the healthy population was incorporated into the PBPK model. By using the reverse translational tool and a retrograde approach, the irinotecan metabolism was described by incorporating CYP3A (Km 14.4±1.6 μM, Vmax 14.7± 0.6 pmol/min/mg protein) and an additional clearance. This additional clearance was then explained by scaled esterases (CES1 and CES2) enzyme kinetic from in vitro data. Besides the clinical study that was used to build the model, other independent intravenously administered irinotecan studies were used for verification. Results: The model predicted the plasma concentration of the parent drug and its metabolite (SN-38) in good agreement with observed data from the clinical study that was used to build the model, as well as from several clinical studies that were not used to build the initial model, covering a dose range of 50-750 mg/m2. Conclusion: We developed a PBPK model that accurately predicts the pharmacokinetic behavior of irinotecan and its metabolite (SN-38) in an adult cancer population. Future directions involve the adaption of the model to the pediatric cancer population to predict the optimal dose for initiation of first-in-human Phase I studies and to identify potential drug interactions and unanticipated toxicities when this “backbone” regimen is combined with other investigated anticancer agents. This will then lead to an increase in individualized treatment. Citation Format: Kristina Zoran Denic, Sibylle Neuhoff, Joel Reid, Rachel Kudget. A physiologically based pharmacokinetic modeling approach for predicting the exposure of irinotecan and its active metabolite (SN-38) in cancer patients [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A140.

A 20-Year Research Overview: Quantitative Prediction of Hepatic Clearance Using the In Vitro-In Vivo Extrapolation Approach Based on Physiologically Based Pharmacokinetic Modeling and Extended Clearance Concept.

Understanding the extended clearance concept and establishing a physiologically based pharmacokinetic (PBPK) model are crucial for investigating the impact of changes in transporter and metabolizing enzyme abundance/functions on drug pharmacokinetics in blood and tissues. This mini-review provides an overview of the extended clearance concept and a PBPK model that includes transporter-mediated uptake processes in the liver. In general, complete in vitro and in vivo extrapolation (IVIVE) poses challenges due to missing factors that bridge the gap between in vitro and in vivo systems. By considering key in vitro parameters, we can capture in vivo pharmacokinetics, a strategy known as the top-down or middle-out approach. We present the latest progress, theory, and practice of the Cluster Gauss-Newton method, which is used for middle-out analyses. As examples of poor IVIVE, we discuss "albumin-mediated hepatic uptake" and "time-dependent inhibition" of OATP1Bs. The hepatic uptake of highly plasma-bound drugs is more efficient than what can be accounted for by their unbound concentration alone. This phenomenon is referred to as "albumin-mediated" hepatic uptake. IVIVE was improved by measuring hepatic uptake clearance in vitro in the presence of physiologic albumin concentrations. Lastly, we demonstrate the application of Cluster Gauss-Newton method-based analysis to the target-mediated drug disposition of bosentan. Incorporating saturable target binding and OATP1B-mediated hepatic uptake into the PBPK model enables the consideration of nonlinear kinetics across a wide dose range and the prediction of receptor occupancy over time. SIGNIFICANCE STATEMENT: There have been multiple instances where researchers' endeavors to unravel the underlying mechanism of poor in vitro-in vivo extrapolation have led to the discovery of previously undisclosed truths. These include 1) albumin-mediated hepatic uptake, 2) the target-mediated drug disposition in small molecules, and 3) the existence of a trans-inhibition mechanism by inhibitors for OATP1B-mediated hepatic uptake of drugs. Consequently, poor in vitro-in vivo extrapolation and the subsequent inquisitiveness of scientists may serve as a pivotal gateway to uncover hidden mechanisms.

Unraveling Complexities in the Absorption and Disposition Kinetics of Abiraterone via Iterative PBPK Model Development and Refinement.

Abiraterone is a first-in-class inhibitor of cytochrome P450 17A1 (CYP17A1), and its pharmacokinetic (PK) profile is susceptible to intrinsic and extrinsic variabilities. Potential associations between abiraterone concentrations and pharmacodynamic consequences in prostate cancer may demand further dosage optimization to balance therapeutic outcomes. Consequently, we aim to develop a physiologically based pharmacokinetic (PBPK) model for abiraterone via a middle-out approach to prospectively interrogate the untested, albeit clinically relevant, scenarios. To characterize in vivo hydrolysis of prodrug abiraterone acetate (AA) and supersaturation of abiraterone, in vitro aqueous solubility data, biorelevant measurements, and supersaturation and precipitation parameters were utilized for mechanistic absorption simulation. CYP3A4-mediated N-oxidation and sulfotransferase 2A1-catalyzed sulfation of abiraterone were subsequently quantified in human liver subcellular systems. Iterative PBPK model refinement involved evaluation of potential organic anion transporting polypeptide (OATP)-mediated abiraterone uptake in transfected cells in the absence and presence of albumin. The developed PBPK model recapitulated the duodenal concentration-time profile of both AA and abiraterone after simulated AA administration. Our findings established abiraterone as a substrate of hepatic OATP1B3 to recapitulate its unbound metabolic intrinsic clearance. Further consideration of a transporter-induced protein-binding shift established accurate translational scaling factors and extrapolated the sinusoidal uptake process. Subsequent simulations effectively predicted the PK of abiraterone upon single and multiple dosing. Our systematic development of the abiraterone PBPK model has demonstrated its application for the prospective interrogation of the individual or combined influences of potential interindividual variabilities influencing the systemic exposure of abiraterone.

A middle-out approach to foster low-carbon lifestyles

A middle-out approach to foster low-carbon lifestyles

Support for the size-mediated sensitivity hypothesis within a diverse carnivore community.

Carnivore community dynamics are governed by a complex set of often interacting biotic, abiotic and anthropogenic factors that are increasingly volatile as a result of global change. Understanding how these changing conditions influence carnivore communities is urgent because of the important role carnivores play within ecosystems at multiple trophic levels, and the conservation threats that many carnivores face globally. While a great deal of research attention has historically been focused on large carnivores within ecosystems, the size-mediated sensitivity hypothesis has recently been proposed where the smallest carnivore in a system is likely to be the most responsive to the diverse suite of ongoing environmental and anthropogenic changes within ecological communities. We deployed camera traps at 197 sites over 4 years to monitor a diverse suite of mammalian carnivores within the Blue Ridge Mountains of western North Carolina and then used a two-step occupancy modelling-structural equation modelling framework to investigate the relative support for four primary hypothesized drivers (interspecific competition/predation, habitat complexity, food availability and anthropogenic disturbance) on carnivore occurrence. We found that each of the 10 carnivores in our system responded differently to conditions associated with each of these four hypothesized drivers, but that small and medium-sized carnivores had a greater number of significant (p < 0.05) pathways by which these conditions were influencing occupancy relative to large carnivores. In particular, the smallest carnivore observed in our study was the only species for which we found support for each of the four hypothesized drivers influencing occupancy. Collectively, our study supports the size-mediated sensitivity hypothesis and suggests that small carnivores are ideal sentinel species for global change. We echo recent calls for adopting a middle-out approach to investigations into carnivore community dynamics by refocusing sustained monitoring and research efforts on smaller carnivores within systems.

Introducing Matrix for the Reprogramming of Mass Housing Neighbourhoods (MHN) Based on EU Design Taxonomy: The Observatory Case of Serbia

This article addresses the contemporary framework of housing at the EU level in the era of the ‘Housing at the Centre’ approach. More specifically, the research focuses on mass housing neighbourhoods (MHN) as the leading pattern of urban transformation in European cities in the second half of the 20th century, with the intention being to decode the possibilities for its rehabilitation in line with integrated approaches. The article combines (1) a review-based and systematically-oriented approach, in order to provide a state of the art of EU design taxonomy related to the housing issue, and, more specifically, related to MHN, with (2) a comparative study between EU and national design taxonomies, in order to address their conditionality and possible mismatches. The research considers design taxonomy to gain a more comprehensive insight into the content and coherence between programme values and the relevant EU documents (declarations, statements, policy positions, resolutions, reports, communications, charters, action plans, opinions) related to the housing issue, or broader urban issues that include housing as the scope of observation. The taxonomy enables a conceptual methodological framework for a systematic, consistent, and complete description of key research relations. Accordingly, the specific objective of this article is to establish an evaluation framework for reprogramming of MHN based on the EU design taxonomy through (1) the development of the programming matrix for evaluation, which corresponds to the value-based architectural programming model; and (2) introducing Serbian national design taxonomy, in order to demonstrate the anticipation of design values based on the EU taxonomy within the local context. The results indicate the need to examine and test regulatory experimental settings through middle-out approaches, whose central research perspective will be built parallel and coherently through bottom-up inputs, created as the result of collaborative approaches at the community level, and top-down inputs which are the result of the strategic framework established in relation to priorities at the European level.

Prediction of the Drug-Drug Interaction Potential between Tegoprazan and Amoxicillin/Clarithromycin Using the Physiologically Based Pharmacokinetic and Pharmacodynamic Model.

Tegoprazan is a novel potassium-competitive acid blocker. This study investigated the effect of drug-drug interaction on the pharmacokinetics and pharmacodynamics of tegoprazan co-administered with amoxicillin and clarithromycin, the first-line therapy for the eradication of Helicobacter pylori, using physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) modeling. The previously reported tegoprazan PBPK/PD model was modified and applied. The clarithromycin PBPK model was developed based on the model provided by the SimCYP® compound library. The amoxicillin model was constructed using the middle-out approach. All of the observed concentration-time profiles were covered well by the predicted profiles with the 5th and 95th percentiles. The mean ratios of predicted to observed PK parameters, including the area under the curve (AUC), maximum plasma drug concentration (Cmax), and clearance, were within the 30% intervals for the developed models. Two-fold ratios of predicted fold-changes of Cmax and AUC from time 0 to 24 h to observed data were satisfied. The predicted PD endpoints, including median intragastric pH and percentage holding rate at pH above 4 or 6 on day 1 and day 7, were close to the corresponding observed data. This investigation allows evaluation of the effects of CYP3A4 perpetrators on tegoprazan PK and PD changes, thus providing clinicians with the rationale for co-administration dosing adjustment.

Insights in the maturational processes influencing hydrocortisone pharmacokinetics in congenital adrenal hyperplasia patients using a middle-out approach.

Introduction: Hydrocortisone is the standard of care in cortisol replacement therapy for congenital adrenal hyperplasia patients. Challenges in mimicking cortisol circadian rhythm and dosing individualization can be overcome by the support of mathematical modelling. Previously, a non-linear mixed-effects (NLME) model was developed based on clinical hydrocortisone pharmacokinetic (PK) pediatric and adult data. Additionally, a physiologically-based pharmacokinetic (PBPK) model was developed for adults and a pediatric model was obtained using maturation functions for relevant processes. In this work, a middle-out approach was applied. The aim was to investigate whether PBPK-derived maturation functions could provide a better description of hydrocortisone PK inter-individual variability when implemented in the NLME framework, with the goal of providing better individual predictions towards precision dosing at the patient level. Methods: Hydrocortisone PK data from 24 adrenal insufficiency pediatric patients and 30 adult healthy volunteers were used for NLME model development, while the PBPK model and maturation functions of clearance and cortisol binding globulin (CBG) were developed based on previous studies published in the literature. Results: Clearance (CL) estimates from both approaches were similar for children older than 1 year (CL/F increasing from around 150 L/h to 500 L/h), while CBG concentrations differed across the whole age range (CBGNLME stable around 0.5 μM vs. steady increase from 0.35 to 0.8 μM for CBG PBPK). PBPK-derived maturation functions were subsequently included in the NLME model. After inclusion of the maturation functions, none, a part of, or all parameters were re-estimated. However, the inclusion of CL and/or CBG maturation functions in the NLME model did not result in improved model performance for the CL maturation function (ΔOFV > -15.36) and the re-estimation of parameters using the CBG maturation function most often led to unstable models or individual CL prediction bias. Discussion: Three explanations for the observed discrepancies could be postulated, i) non-considered maturation of processes such as absorption or first-pass effect, ii) lack of patients between 1 and 12 months, iii) lack of correction of PBPK CL maturation functions derived from urinary concentration ratio data for the renal function relative to adults. These should be investigated in the future to determine how NLME and PBPK methods can work towards deriving insights into pediatric hydrocortisone PK.

Law, Socio-Legal Governance, the Internet of Things, and Industry 4.0: A Middle-Out/Inside-Out Approach

The Web of Data, the Internet of Things, and Industry 4.0 are converging, and society is challenged to ensure that appropriate regulatory responses can uphold the rule of law fairly and effectively in this emerging context. The challenge extends beyond merely submitting digital processes to the law. We contend that the 20th century notion of ‘legal order’ alone will not be suitable to produce the social order that the law should bring. The article explores the concepts of rule of law and of legal governance in digital and blockchain environments. We position legal governance from an empirical perspective, i.e., as an explanatory and validation concept to support the implementation of the rule of law in the new digital environments. As a novel contribution, this article (i) progresses some of the work done on the metarule of law and complements the SMART middle-out approach with an inside-out approach to digital regulatory systems and legal compliance models; (ii) sets the state-of-the-art and identifies the way to explain and validate legal information flows and hybrid agents’ behaviour; (iii) describes a phenomenological and historical approach to legal and political forms; and (iv) shows the utility of separating enabling and driving regulatory systems.

Non-human Personas. Including Nature in the Participatory Design of Smart Cities

For the past decades humans have been placed at the centre of designing information and communication technologies (ICT), leading to the rise in prominence of human-centred design. The field of smart cities has equally adopted notions of citizen participation as a way to ensure that technological solutions improve people’s livelihoods. However, these kinds of processes treat the urban environment as separate from nature, promoting human comfort and convenience over planetary health and wellbeing. Motivated by these growing concerns that highlight the urgency to reconsider how we define and practice participation in smart cities and in human-centred ICT solutions more broadly, this article assesses how the personas method can be adapted to include more-than-human perspectives in the design process. Based on a case study, which involved designing smart urban furniture for human and non-human use, we introduce a framework for developing and employing non-human personas. As a key element of the framework, we describe a middle-out approach for forming a coalition that can speak on behalf of the non-human species that are impacted by design decisions. We demonstrate how the framework can be used through its retrospective application on two research-led smart city projects. The article concludes with a discussion of key principles for creating and using non-human personas in design projects.

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions

PurposeTo build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs).MethodsThe Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model.ResultsThe carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUClast ratios (AUClast during DDI/AUClast without co-administration) and DDI Cmax ratios (Cmax during DDI/Cmax without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values.ConclusionsA whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

Non-human Personas. Including Nature in the Participatory Design of Smart Cities

For the past decades humans have been placed at the centre of designing information and communication technologies (ICT), leading to the rise in prominence of human-centred design. The field of smart cities has equally adopted notions of citizen participation as a way to ensure that technological solutions improve people’s livelihoods. However, these kinds of processes treat the urban environment as separate from nature, promoting human comfort and convenience over planetary health and wellbeing. Motivated by these growing concerns that highlight the urgency to reconsider how we define and practice participation in smart cities and in human-centred ICT solutions more broadly, this article assesses how the personas method can be adapted to include more-than-human perspectives in the design process. Based on a case study, which involved designing smart urban furniture for human and non-human use, we introduce a framework for developing and employing non-human personas. As a key element of the framework, we describe a middle-out approach for forming a coalition that can speak on behalf of the non-human species that are impacted by design decisions. We demonstrate how the framework can be used through its retrospective application on two research-led smart city projects. The article concludes with a discussion of key principles for creating and using non-human personas in design projects.

Lost in modelling and simulation?

Over the past few decades, physiologically-based pharmacokinetic modelling (PBPK) has been anticipated to be a powerful tool to improve the productivity of drug discovery and development. However, recently, multiple systematic evaluation studies independently suggested that the predictive power of current oral absorption (OA) PBPK models needs significant improvement. There is some disagreement between the industry and regulators about the credibility of OA PBPK modelling. Recently, the editorial board of AMDET&DMPK has announced the policy for the articles related to PBPK modelling (Modelling and simulation ethics). In this feature article, the background of this policy is explained: (1) Requirements for scientific writing of PBPK modelling, (2) Scientific literacy for PBPK modelling, and (3) Middle-out approaches. PBPK models are a useful tool if used correctly. This article will hopefully help advance the science of OA PBPK models.

Understanding Mechanisms of Food Effect and Developing Reliable PBPK Models Using a Middle-out Approach

Over the last 10years, 40% of approved oral drugs exhibited a significant effect of food on their pharmacokinetics (PK) and currently the only method to characterize the effect of food on drug absorption, which is recognized by the authorities, is to conduct a clinical evaluation. Within the pharmaceutical industry, there is a significant effort to predict the mechanism and clinical relevance of a food effect. Physiologically based pharmacokinetic (PBPK) models combining both drug-specific and physiology-specific data have been used to predict the effect of food on absorption and to reveal the underlying mechanisms. This manuscript provides detailed descriptions of how a middle-out modeling approach, combining bottom-up in vitro-based predictions with limited top-down fitting of key model parameters for clinical data, can be successfully used to predict the magnitude and direction of food effect when it is predicted poorly by abottom-up approach. For nefazodone, a mechanistic clearance for the gut and liver was added, for furosemide, an absorption window was introduced, and for aprepitant, the biorelevant solubility was refined using multiple solubility measurements. In all cases, these adjustments were supported by literature data and showcased a rational approach to assess the factors limiting absorption and exposure.

A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines.

Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model’s predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic.

Establishing virtual bioequivalence and clinically relevant specifications using in vitro biorelevant dissolution testing and physiologically-based population pharmacokinetic modeling. case example: Naproxen

BackgroundPhysiologically-based population pharmacokinetic modeling (popPBPK) coupled with in vitro biopharmaceutics tools such as biorelevant dissolution testing can serve as a powerful tool to establish virtual bioequivalence and set clinically relevant specifications. One of several applications of popPBPK modeling is in the emerging field of virtual bioequivalence (VBE), where it can be used to streamline drug development by implementing model-informed formulation design and to inform regulatory decision-making e.g., with respect to evaluating the possibility of extending BCS-based biowaivers beyond BCS Class I and III compounds in certain cases. MethodsIn this study, Naproxen, a BCS class II weak acid was chosen as the model compound. In vitro biorelevant solubility and dissolution experiments were performed and the resulting data were used as an input to the PBPK model, following a stepwise workflow for the confirmation of the biopharmaceutical parameters. The naproxen PBPK model was developed by implementing a middle-out approach and verified against clinical data obtained from the literature. Once confidence in the performance of the model was achieved, several in vivo dissolution scenarios, based on model-based analysis of the in vitro data, were used to simulate clinical trials in healthy adults. Inter-occasion variability (IOV) was also added to critical physiological parameters and mechanistically propagated through the simulations. The various trials were simulated on a “worst/best case” dissolution scenario and average bioequivalence was assessed according to Cmax, AUC and Tmax. ResultsVBE results demonstrated that naproxen products with in vitro dissolution reaching 85% dissolved within 90 min would lie comfortably within the bioequivalence limits for Cmax and AUC. Based on the establishment of VBE, a dissolution “safe space” was designed and a clinically relevant specification for naproxen products was proposed. The interplay between formulation-related and drug-specific PK parameters (e.g., t1/2) to predict the in vivo performance was also investigated. ConclusionOver a wide range of values, the in vitro dissolution rate is not critical for the clinical performance of naproxen products and therefore naproxen could be eligible for BCS-based biowaivers based on in vitro dissolution under intestinal conditions. This approach may also be applicable to other poorly soluble acidic compounds with long half-lives, providing an opportunity to streamline drug development and regulatory decision-making without putting the patient at a risk.