Model-based closed-loop control of thalamic deep brain stimulation

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

Aerobic metabolic scope mapping of an invasive fish species with global warming

Climate change will exacerbate the negative effects associated with the introduction of non-indigenous species in marine ecosystems. Predicting the spread of invasive species in relation to environmental warming is therefore a fundamental task in ecology and conservation. The Baltic Sea is currently threatened by several local stressors and the highest increase in sea surface temperature of the world's large marine ecosystems. These new thermal conditions can further favour the spreading of the invasive round goby (Neogobius melanostomus), a fish of Ponto-Caspian origin, currently well established in the southern and central parts of the Baltic Sea. This study aims to assess the thermal habitat suitability of the round goby in the Baltic Sea considering the past and future conditions. The study combines sightings records with known physiological models of aerobic performance and sea surface temperatures. Physiological models read these temperatures, at sighting times and locations, to determine their effects on the aerobic metabolic scope (AMS) of the fish, a measure of its energetic potential in relation to environmental conditions. The geographical mapping of the AMS was used to describe the changes in habitat suitability during the past 3 decades and for climatic predictions (until 2100) showing that the favourable thermal habitat in the Baltic Sea has increased during the past 32 years and will continue to do so in all the applied climate model predictions. Particularly, the predicted new thermal conditions do not cause any reduction in the AMS of round goby populations, while the wintertime cold ranges are likely expected to preserve substantial areas from invasion. The results of this research can guide future monitoring programs increasing the chance to detect this invader in novel areas.

Real-time tracking of fibrinolysis under constant wall shear and various pulsatile flows in an in-vitro thrombolysis model

A great need exists for the development of a more representative in-vitro model to efficiently screen novel thrombolytic therapies. We herein report the design, validation, and characterization of a highly reproducible, physiological scale, flowing clot lysis platform with real-time fibrinolysis monitoring to screen thrombolytic drugs utilizing a fluorescein isothiocyanate (FITC)-labeled clot analog. Using this Real-Time Fluorometric Flowing Fibrinolysis assay (RT-FluFF assay), a tPa-dependent degree of thrombolysis was observed both via clot mass loss as well as fluorometrically monitored release of FITC-labeled fibrin degradation products. Percent clot mass loss ranged from 33.6% to 85.9% with fluorescence release rates of 0.53 to 1.17 RFU/min in 40 and 1000 ng/mL tPa conditions, respectively. The platform is easily adapted to produce pulsatile flows. Hemodynamics of human main pulmonary artery were mimicked through matching dimensionless flow parameters calculated using clinical data. Increasing pressure amplitude range (4-40 mmHg) results in a 20% increase of fibrinolysis at 1000 ng/mL tPA. Increasing shear flow rate (205-913 s^-1) significantly increases fibrinolysis and mechanical digestion. These findings suggest pulsatile level affects thrombolytic drug activities and the proposed in-vitro clot model offers a versatile testing platform for thrombolytic drug screening.

Modeling the physiological roles of the heart and kidney in heart failure with preserved ejection fraction during baroreflex activation therapy

Heart failure (HF) is a leading cause of death and is increasing in prevalence. Unfortunately, therapies that have been efficacious in patients with HF with reduced ejection fraction (HFrEF) have not convincingly shown a reduction in cardiovascular mortality in patients with HF with preserved ejection fraction (HFpEF). It is thought that high sympathetic nerve activity (SNA) in the heart plays a role in HF progression. Clinical trials demonstrate that baroreflex activation therapy reduces left ventricular (LV) mass and blood pressure (BP) in patients with HFpEF and hypertension; however, the mechanisms are unclear. In the present study, we used HumMod, a large physiology model to simulate HFpEF and predict the time-dependent changes in systemic and cardiac hemodynamics, SNA, and cardiac stresses during baroreflex activation. The baseline HFpEF model was associated with elevations in systolic BP, diastolic dysfunction, and LV hypertrophy and stiffness similar to clinical HFpEF. Simulating 12 mo of baroreflex activation resulted in reduced systolic BP (-25 mmHg) and LV mass (-15%) similar to clinical evidence. Baroreflex activation also resulted in sustained decreases in cardiac and renal SNA (-22%) and improvement in LV β1-adrenergic function. However, the baroreflex-induced reductions in BP and improvements in cardiac stresses, mass, and function were mostly attenuated when renal SNA was clamped at baseline levels. These simulations suggest that the suppression of renal SNA could be a primary determinant of the cardioprotective effects from baroreflex activation in HFpEF.NEW & NOTEWORTHY Treatments that are efficacious in patients with HFrEF have not shown a significant impact on cardiovascular mortality in patients with HFpEF. We believe these simulations offer novel insight into the important roles of the cardiac and renal nerves in HFpEF and the potential mechanisms of how baroreflex activation alleviates HFpEF disease progression.

Inhibitory and stimulatory micropeptides preferentially bind to different conformations of the cardiac calcium pump

The ATP-dependent ion pump sarco/endoplasmic reticulum Ca²⁺-ATPase (SERCA) sequesters Ca²⁺ in the endoplasmic reticulum to establish a reservoir for cell signaling. Because of its central importance in physiology, the activity of this transporter is tightly controlled via direct interactions with tissue-specific regulatory micropeptides that tune SERCA function to match changing physiological conditions. In the heart, the micropeptide phospholamban (PLB) inhibits SERCA, while dwarf open reading frame (DWORF) stimulates SERCA. These competing interactions determine cardiac performance by modulating the amplitude of Ca²⁺ signals that drive the contraction/relaxation cycle. We hypothesized that the functions of these peptides may relate to their reciprocal preferences for SERCA binding; SERCA binds PLB more avidly at low cytoplasmic [Ca²⁺] but binds DWORF better when [Ca²⁺] is high. In the present study, we demonstrated this opposing Ca²⁺ sensitivity is due to preferential binding of DWORF and PLB to different intermediate states that SERCA samples during the Ca²⁺ transport cycle. We show PLB binds best to the SERCA E1-ATP state, which prevails at low [Ca²⁺]. In contrast, DWORF binds most avidly to E1P and E2P states that are more populated when Ca²⁺ is elevated. Moreover, FRET microscopy revealed dynamic shifts in SERCA–micropeptide binding equilibria during cellular Ca²⁺ elevations. A computational model showed that DWORF exaggerates changes in PLB–SERCA binding during the cardiac cycle. These results suggest a mechanistic basis for inhibitory versus stimulatory micropeptide function, as well as a new role for DWORF as a modulator of dynamic oscillations of PLB–SERCA regulatory interactions.

Inflection point in climatic suitability of insect pest species in Europe suggests non-linear responses to climate change

Climate change and globalization affect the suitable conditions for agricultural crops and insect pests, threatening future food security. It remains unknown whether shifts in species' climatic suitability will be linear or rather non-linear, with crop exposure to pests suddenly increasing when a critical temperature threshold is crossed. Moreover, uncertainty of forecasts can arise because of the modelling approach based either on species distribution data or on physiological measurements. Here, we compared the predictions of two modelling approaches (physiological models and species distribution models) for forecasting the potential distribution of agricultural insect pests in Europe. Despite conceptual differences, we found good agreement overall between the two approaches. We further identified a potential regime change in pest pressure along a temperature gradient. With both modelling approaches, we found an inflection point in the number of pest species with suitable climatic conditions around a minimum temperature of the coldest month of -3°C. Our results could help decision-makers anticipate the onset of rising pest pressure and provide support for intensifying surveillance measures, particularly in regions where temperatures are already beyond the inflection point.

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay

The in vitro-in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CL_tot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CL_int) prediction. Herein, we directly compare these two approaches for predicting CL_tot in rats. A structurally diverse set of 1114 compounds with known in vivo CL_tot, in vitro CL_int, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CL_tot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (f_u,p), and CL_int. CL_tot prediction utilizing in vitro CL_int and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CL_tot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cf_u,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.

Practical Use of Regularization in Individualizing a Mathematical Model of Cardiovascular Hemodynamics Using Scarce Data

Individualizing physiological models to a patient can enable patient-specific monitoring and treatment in critical care environments. However, this task often presents a unique "practical identifiability" challenge due to the conflict between model complexity and data scarcity. Regularization provides an established framework to cope with this conflict by compensating for data scarcity with prior knowledge. However, regularization has not been widely pursued in individualizing physiological models to facilitate patient-specific critical care. Thus, the goal of this work is to garner potentially generalizable insight into the practical use of regularization in individualizing a complex physiological model using scarce data by investigating its effect in a clinically significant critical care case study of blood volume kinetics and cardiovascular hemodynamics in hemorrhage and circulatory resuscitation. We construct a population-average model as prior knowledge and individualize the physiological model via regularization to illustrate that regularization can be effective in individualizing a physiological model to learn salient individual-specific characteristics (resulting in the goodness of fit to individual-specific data) while restricting unnecessary deviations from the population-average model (achieving practical identifiability). We also illustrate that regularization yields parsimonious individualization of only sensitive parameters as well as adequate physiological plausibility and relevance in predicting internal physiological states.

A biomechanical model of the human defecatory system to investigate mechanisms of continence

This article presents a method to fabricate, measure and control a physical simulation of the human defecatory system to investigate individual and combined effects of anorectal angle and sphincter pressure on continence. To illustrate the capabilities and clinical relevance of the work, the influence of a passive-assistive artificial anal sphincter (FENIX^TM) is evaluated. A model rectum and associated soft tissues, based on geometry from an anonymised computed tomography dataset, was fabricated from silicone and showed behavioural realism to the biological system and ex vivo tissue. Simulated stool matter with similar rheological properties to human faeces was developed. Instrumentation and control hardware were used to regulate injection of simulated stool into the system, automate balloon catheter movement through the anal canal, define the anorectal angle and monitor stool flow rate, intra-rectal pressure, anal canal pressure and puborectalis force. Studies were conducted to examine the response of anorectal angles at 80°, 90° and 100° with simulated stool. Tests were then repeated with the inclusion of a FENIX device. Stool leakage was reduced as the anorectal angle became more acute. Conversely, intra-rectal pressure increased. Overall inclusion of the FENIX reduced faecal leakage, while combined effects of the FENIX and an acute anorectal angle showed the greatest resistance to faecal leakage. These data demonstrate that the anorectal angle and sphincter pressure are fundamental in maintaining continence. Furthermore, it demonstrates that use of the FENIX can increase resistance to faecal leakage and reduce anorectal angles required to maintain continence. Physical simulation of the defecatory system is an insightful tool to better understand, in a quantitative manner, the effects of the anorectal angle and sphincter pressure on continence. This work is valuable in helping improve our understanding of the physical behaviour of the continence mechanism and facilitating improved technologies to treat severe faecal incontinence.

Mechanistic Physiologically Based Pharmacokinetic (PBPK) Model of the Heart Accounting for Inter-Individual Variability: Development and Performance Verification

Modern model-based approaches to cardiac safety and efficacy assessment require accurate drug concentration-effect relationship establishment. Thus, knowledge of the active concentration of drugs in heart tissue is desirable along with inter-subject variability influence estimation. To that end, we developed a mechanistic physiologically based pharmacokinetic model of the heart. The models were described with literature-derived parameters and written in R, v.3.4.0. Five parameters were estimated. The model was fitted to amitriptyline and nortriptyline concentrations after an intravenous infusion of amitriptyline. The cardiac model consisted of 5 compartments representing the pericardial fluid, heart extracellular water, and epicardial intracellular, midmyocardial intracellular, and endocardial intracellular fluids. Drug cardiac metabolism, passive diffusion, active efflux, and uptake were included in the model as mechanisms involved in the drug disposition within the heart. The model accounted for inter-individual variability. The estimates of optimized parameters were within physiological ranges. The model performance was verified by simulating 5 clinical studies of amitriptyline intravenous infusion, and the simulated pharmacokinetic profiles agreed with clinical data. The results support the model feasibility. The proposed structure can be tested with the goal of improving the patient-specific model-based cardiac safety assessment and offers a framework for predicting cardiac concentrations of various xenobiotics.

The Constraints, Construction, and Verification of a Strain-Specific Physiologically Based Pharmacokinetic Rat Model

The use of in vitro-in vivo extrapolation (IVIVE) techniques, mechanistically incorporated within physiologically based pharmacokinetic (PBPK) models, can harness in vitro drug data and enhance understanding of in vivo pharmacokinetics. This study's objective was to develop a user-friendly rat (250 g, male Sprague-Dawley) IVIVE-linked PBPK model. A 13-compartment PBPK model including mechanistic absorption models was developed, with required system data (anatomical, physiological, and relevant IVIVE scaling factors) collated from literature and analyzed. Overall, 178 system parameter values for the model are provided. This study also highlights gaps in available system data required for strain-specific rat PBPK model development. The model's functionality and performance were assessed using previous literature-sourced in vitro properties for diazepam, metoprolol, and midazolam. The results of simulations were compared against observed pharmacokinetic rat data. Predicted and observed concentration profiles in 10 tissues for diazepam after a single intravenous (i.v.) dose making use of either observed i.v. clearance (CL_iv) or in vitro hepatocyte intrinsic clearance (CL_int) for simulations generally led to good predictions in various tissue compartments. Overall, all i.v. plasma concentration profiles were successfully predicted. However, there were challenges in predicting oral plasma concentration profiles for metoprolol and midazolam, and the potential reasons and according solutions are discussed.

A Mechanism-Based Pharmacokinetic Enzyme Turnover Model for Dichloroacetic Acid Autoinhibition in Rats

Dichloroacetic acid (DCA), a halogenated organic acid, is a pyruvate dehydrogenase kinase inhibitor that has been used to treat congenital or acquired lactic acidosis and is currently in early-phase clinical trials for cancer treatment. DCA was found to inhibit its own metabolism by irreversibly inactivating glutathione transferase zeta 1 (GSTZ1-1), resulting in nonlinear kinetics and abnormally high accumulation ratio after repeated dosing. In this analysis, a semi-mechanistic pharmacokinetic enzyme turnover model was developed for the first time to capture DCA autoinhibition, gastrointestinal region-dependent absorption, and time-dependent change in bioavailability in rats. The maximum rate constant for DCA-induced GSTZ1-1 inactivation is estimated to be 0.96/h, which is 110 times that of the rate constant for GSTZ1-1 natural degradation (0.00875/h). The model-predicted DCA concentration that corresponds to 50% of maximum enzyme inhibition (EC₅₀) is 4.32 mg/L. The constructed pharmacokinetic enzyme turnover model, when applied to human data, could be used to predict the accumulation of DCA after repeated oral dosing, guide selection of dosing regimens in clinical studies, and facilitate clinical development of DCA.

Physiologically Based Absorption Modeling to Design Extended-Release Clinical Products for an Ester Prodrug

Absorption modeling has demonstrated its great value in modern drug product development due to its utility in understanding and predicting in vivo performance. In this case, we integrated physiologically based modeling in the development processes to effectively design extended-release (ER) clinical products for an ester prodrug LY545694. By simulating the trial results of immediate-release products, we delineated complex pharmacokinetics due to prodrug conversion and established an absorption model to describe the clinical observations. This model suggested the prodrug has optimal biopharmaceutical properties to warrant developing an ER product. Subsequently, we incorporated release profiles of prototype ER tablets into the absorption model to simulate the in vivo performance of these products observed in an exploratory trial. The models suggested that the absorption of these ER tablets was lower than the IR products because the extended release from the formulations prevented the drug from taking advantage of the optimal absorption window. Using these models, we formed a strategy to optimize the ER product to minimize the impact of the absorption window limitation. Accurate prediction of the performance of these optimized products by modeling was confirmed in a third clinical trial.

Exploration of PBPK Model-Calculation of Drug Time Course in Tissue Using IV Bolus Drug Plasma Concentration-Time Profile and the Physiological Parameters of the Organ

An uncommon innovative consideration of the well-stirred linear physiologically based pharmacokinetic model and the drug plasma concentration-time profile, which is measured in routine intravenous bolus pharmacokinetic study, was applied for the calculation of the drug time course in human tissues. This cannot be obtained in the in vivo pharmacokinetic study. The physiological parameters of the organ such as organ tissue volume, organ blood flow rate, and its vascular volume were used in the calculation. The considered method was applied to calculate the time course of midazolam, alprazolam, quinidine, and diclofenac in human organs or tissues. The suggested method might be applied for the prediction of drug concentration-time profile in tissues and consequently the drug concentration level in the targeted tissue, as well as the possible undesirable toxic levels in other tissues.

Understanding Subcutaneous Tissue Pressure for Engineering Injection Devices for Large-Volume Protein Delivery

Subcutaneous injection allows for self-administration of monoclonal antibodies using prefilled syringes, autoinjectors, and on-body injector devices. However, subcutaneous injections are typically limited to 1 mL due to concerns of injection pain from volume, viscosity, and formulation characteristics. Back pressure can serve as an indicator for changes in subcutaneous mechanical properties leading to pain during injection. The purpose of this study was to investigate subcutaneous pressures and injection site reactions as a function of injection volume and flow rate. A pressure sensor in the fluid path recorded subcutaneous pressures in the abdomen of Yorkshire swine. The subcutaneous tissue accommodates large-volume injections and with little back pressure as long as low flow rates are used. A 1 mL injection in 10 seconds (360 mL/h flow rate) generated a pressure of 24.0 ± 3.4 kPa, whereas 10 mL delivered in 10 minutes (60 mL/h flow rate) generated a pressure of 7.4 ± 7.8 kPa. After the injection, the pressure decays to 0 over several seconds. The subcutaneous pressures and mechanical strain increased with increasing flow rate but not increasing dose volume. These data are useful for the design of injection devices to mitigate back pressure and pain during subcutaneous large-volume injection.

Toward systems-level analysis of agricultural production from crassulacean acid metabolism (CAM): scaling from cell to commercial production

Systems-level analyses have become prominent tools for assessing the yield, viability, economic consequences and environmental impacts of agricultural production. Such analyses are well-developed for many commodity crops that are used for food and biofuel, but have not been developed for agricultural production systems based on drought-tolerant plants that use crassulacean acid metabolism (CAM). We review the components of systems-level evaluations, and identify the information available for completing such analyses for CAM cropping systems. Specific needs for developing systems-level evaluations of CAM agricultural production include: improvement of physiological models; assessment of product processing after leaving the farm gate; and application of newly available genetic tools to the optimization of CAM species for commercial production.

A Novel Method for Assessing Drug Degradation Product Safety Using Physiologically-Based Pharmacokinetic Models and Stochastic Risk Assessment

Patient safety risk due to toxic degradation products is a potentially critical quality issue for a small group of useful drug substances. Although the pharmacokinetics of toxic drug degradation products may impact product safety, these data are frequently unavailable. The objective of this study is to incorporate the prediction capability of physiologically based pharmacokinetic (PBPK) models into a rational drug degradation product risk assessment procedure using a series of model drug degradants (substituted anilines). The PBPK models were parameterized using a combination of experimental and literature data and computational methods. The impact of model parameter uncertainty was incorporated into stochastic risk assessment procedure for estimating human safe exposure levels based on the novel use of a statistical metric called "PROB" for comparing probability that a human toxicity-target tissue exposure exceeds the rat exposure level at a critical no-observed-adverse-effect level. When compared with traditional risk assessment calculations, this novel PBPK approach appeared to provide a rational basis for drug instability risk assessment by focusing on target tissue exposure and leveraging physiological, biochemical, biophysical knowledge of compounds and species.

Application and validation of an advanced gastrointestinal in vitro model for the evaluation of drug product performance in pharmaceutical development

Methods to understand and predict the oral bioavailability of drug products are a prioritized research area within the pharmaceutical industry. Models to predict oral bioavailability have the potential to reduce risk, time, and cost in development as well as decrease the need for animal studies. The TNO intestinal model (TIM-1) is an advanced dissolution model deployed by AstraZeneca since 2008. This article presents a systematic evaluation of TIM-1 against in vivo data. The relative performance of compounds and formulations tested in TIM-1 and in vivo was compared both by a qualitative analysis and a linear regression analysis of relative exposure measures between test and reference formulations in TIM-1 and in vivo. The TIM-1 correctly predicted in vivo rank order in 84% and 79% of cases for AUC and Cmax , respectively, when using the 3-h time point. There was only one case for Cmax in which TIM-1 did not predict an in vivo difference. The correlation coefficient (R(2) ) between relative (test vs. reference formulations) fraction available in TIM-1 after 3 h and AUC was 0.78. Thus, this study suggests that the TIM-1 system can be used to assess the risk for significant differences in exposure between formulations and compound modifications.

An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes

This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.

Incorporation of physiologically based pharmacokinetic modeling in the evaluation of solubility requirements for the salt selection process: a case study using phenytoin

In the pharmaceutical industry, salt is commonly used to improve the oral bioavailability of poorly soluble compounds. Currently, there is a limited understanding on the solubility requirement for salts that will translate to improvement in oral exposure. Despite the obvious need, there is very little research reported in this area mainly due to the complexity of such a system. To our knowledge, no report has been published to guide this important process and salt solubility requirement still remains unanswered. Physiologically based pharmacokinetic (PBPK) modeling offers a means to dynamically integrate the complex interplay of the processes determining oral absorption. A sensitivity analysis was performed using a PBPK model describing phenytoin to determine a solubility requirement for phenytoin salts needed to achieve optimal oral bioavailability for a given dose. Based on the analysis, it is predicted that phenytoin salts with solubility greater than 0.3 mg/mL would show no further increases in oral bioavailability. A salt screen was performed using a variety of phenytoin salts. The piperazine and sodium salts showed the lowest and highest aqueous solubility and were tested in vivo. Consistent with our analysis, we observed no significant differences in oral bioavailability for these two salts despite an approximate 60 fold difference in solubility. Our study illustrates that higher solubility salts sometimes provide no additional improvements in oral bioavailability and PBPK modeling can be utilized as an important tool to provide guidance to the salt selection and define a salt solubility requirement.

Development of a hybrid physiologically based pharmacokinetic model with drug-specific scaling factors in rat to improve prediction of human pharmacokinetics

Accurate prediction of pharmacokinetics (PK) in humans has been a vital part of drug discovery. The aims of this study are to verify the usefulness of scaling factors for clearance (CL) and apparent volume of distribution at the steady state (Vss ) estimated from the difference between observed and predicted PK profiles in rats for human PK prediction, and to develop a novel hybrid physiologically based pharmacokinetic (PBPK) model with the two scaling factors. The human prediction accuracies for CL with in vitro-in vivo extrapolation and Vss with a tissue composition model were improved by using rat-scaling factors. This improvement was explainable by data that the scaling factors for CL and Vss in rats were correlated with those in humans. The predictability of plasma concentration-time profiles by the hybrid PBPK model incorporating two scaling factors was compared mainly with that by the conventional PBPK model. The hybrid PBPK model yielded higher prediction accuracy for plasma concentrations than the conventional method. Furthermore, we proposed a tiered approach using the three prediction methods, including the hybrid Dedrick approach, that were previously reported (Sayama H, Komura H, Kogayu M. 2013. Drug Metab Dispos 41:498-507), taking the available information in the individual stages of drug discovery and development into consideration.

A mechanistic physiologically based pharmacokinetic-enzyme turnover model involving both intestine and liver to predict CYP3A induction-mediated drug-drug interactions

Cytochrome P450 (CYP) 3A induction-mediated drug-drug interaction (DDI) is one of the major concerns in drug development and clinical practice. The aim of the present study was to develop a novel mechanistic physiologically based pharmacokinetic (PBPK)-enzyme turnover model involving both intestinal and hepatic CYP3A induction to quantitatively predict magnitude of CYP3A induction-mediated DDIs from in vitro data. The contribution of intestinal P-glycoprotein (P-gp) was also incorporated into the PBPK model. First, the pharmacokinetic profiles of three inducers and 14 CYP3A substrates were predicted successfully using the developed model, with the predicted area under the plasma concentration-time curve (AUC) [area under the plasma concentration-time curve] and the peak concentration (Cmax ) [the peak concentration] in accordance with reported values. The model was further applied to predict DDIs between the three inducers and 14 CYP3A substrates. Results showed that predicted AUC and Cmax ratios in the presence and absence of inducer were within twofold of observed values for 17 (74%) of the 23 DDI studies, and for 14 (82%) of the 17 DDI studies, respectively. All the results gave us a conclusion that the developed mechanistic PBPK-enzyme turnover model showed great advantages on quantitative prediction of CYP3A induction-mediated DDIs.

Issues, challenges, and opportunities in model-based drug development for monoclonal antibodies

Over the last two decades, there has been a simultaneous explosion in the levels of activity and capability in both monoclonal antibody (mAb) drug development and in the use of quantitative pharmacologic models to facilitate drug development. Both of these topics are currently areas of great interest to academia, the pharmaceutical and biotechnology industries, and to regulatory authorities. In this article, we summarize convergence of these two areas and discuss some of the current and historical applications of the use of mathematical-model-based techniques to facilitate the discovery and development of mAb therapeutics. We also consider some of the current issues and limitations in model-based antibody discovery/development and highlight areas of further opportunity.

Using chromosome introgression lines to map quantitative trait loci for photosynthesis parameters in rice (Oryza sativa L.) leaves under drought and well-watered field conditions

Photosynthesis is fundamental to biomass production, but sensitive to drought. To understand the genetics of leaf photosynthesis, especially under drought, upland rice cv. Haogelao, lowland rice cv. Shennong265, and 94 of their introgression lines (ILs) were studied at flowering and grain filling under drought and well-watered field conditions. Gas exchange and chlorophyll fluorescence measurements were conducted to evaluate eight photosynthetic traits. Since these traits are very sensitive to fluctuations in microclimate during measurements under field conditions, observations were adjusted for microclimatic differences through both a statistical covariant model and a physiological approach. Both approaches identified leaf-to-air vapour pressure difference as the variable influencing the traits most. Using the simple sequence repeat (SSR) linkage map for the IL population, 1-3 quantitative trait loci (QTLs) were detected per trait-stage-treatment combination, which explained between 7.0% and 30.4% of the phenotypic variance of each trait. The clustered QTLs near marker RM410 (the interval from 57.3 cM to 68.4 cM on chromosome 9) were consistent over both development stages and both drought and well-watered conditions. This QTL consistency was verified by a greenhouse experiment under a controlled environment. The alleles from the upland rice at this interval had positive effects on net photosynthetic rate, stomatal conductance, transpiration rate, quantum yield of photosystem II (PSII), and the maximum efficiency of light-adapted open PSII. However, the allele of another main QTL from upland rice was associated with increased drought sensitivity of photosynthesis. These results could potentially be used in breeding programmes through marker-assisted selection to improve drought tolerance and photosynthesis simultaneously.

Physiologically based pharmacokinetic tissue compartment model selection in drug development and risk assessment

A well-stirred tank (WST) has been the predominant flow-limited tissue compartment model in physiologically based pharmacokinetic (PBPK) modeling. Recently, we developed a two-region asymptotically reduced (TAR) PBPK tissue compartment model through an asymptotic approximation to a two-region vascular-extravascular system to incorporate more biophysical detail than the WST model. To determine the relevance of a flow-limited TAR (F-TAR) approach, 75 structurally diverse drugs were evaluated herein using a priori predicted tissue:plasma partition coefficients along with hybrid and whole-body PBPK of eight rat tissues to determine the impact of model selection on simulation and optimization. Simulations showed that the F-TAR model significantly improved the ability to predict drug exposure, with hybrid and whole-body WST model error approaching 50% for tissues with larger vascular volumes. When optimization was used to fit F-TAR and WST models to pseudo data, WST-optimized drug partition coefficients more appropriately represented curve-fitting parameters rather than biophysically meaningful partition coefficients. Median F-TAR-optimized error ranged from -0.4% to +0.3%, whereas WST-optimized median error ranged from -22.2% to +1.8%. These studies demonstrated that the use of F-TAR represents a more accurate, biophysically realistic PBPK tissue model for predicting tissue exposure to drug and that it should be considered for use in drug development and regulatory review.

A physiological model of the effect of hypoglycemia on plasma potassium

BACKGROUND

Adrenaline release and excess insulin during hypoglycemia stimulate the uptake of potassium from the bloodstream, causing low plasma potassium (hypokalemia). Hypokalemia has a profound effect on the heart and is associated with an increased risk of malignant cardiac arrhythmias. It is the aim of this study to develop a physiological model of potassium changes during hypoglycemia to better understand the effect of hypoglycemia on plasma potassium.

METHOD

Potassium counterregulation to hypokalemia was modeled as a linear function dependent on the absolute potassium level. An insulin-induced uptake of potassium was modeled using a negative exponential function, and an adrenaline-induced uptake of potassium was modeled as a linear function. Functional expressions for the three components were found using published data.

RESULTS

The performance of the model was evaluated by simulating plasma potassium from three published studies. Simulations were done using measured levels of adrenaline and insulin. The mean root mean squared error (RMSE) of simulating plasma potassium from the three studies was 0.09 mmol/liter, and the mean normalized RMSE was 14%. The mean difference between nadirs in simulated and measured potassium was 0.12 mmol/liter.

CONCLUSIONS

The presented model simulated plasma potassium with good accuracy in a wide range of clinical settings. The limited number of hypoglycemic episodes in the test set necessitates further tests to substantiate the ability of the model to simulate potassium during hypoglycemia. In conclusion, the model is a good first step toward better understanding of changes in plasma potassium during hypoglycemia.

Biomass recycling and the origin of phenotype in fungal mycelia

Fungi are one of the most important and widespread components of the biosphere, and are essential for the growth of over 90% of all vascular plants. Although they are a separate kingdom of life, we know relatively little about the origins of their ubiquitous existence. This reflects a wider ignorance arising from their status as indeterminate organisms epitomized by extreme phenotypic plasticity that is essential for survival in complex environments. Here we show that the fungal phenotype may have its origins in the defining characteristic of indeterminate organisms, namely their ability to recycle locally immobilized internal resources into a mobilized form capable of being directed to new internal sinks. We show that phenotype can be modelled as an emergent phenomenon resulting from the interplay between simple local processes governing uptake and remobilization of internal resources, and macroscopic processes associated with their transport. Observed complex growth forms are reproduced and the sensitive dependence of phenotype on environmental context may be understood in terms of nonlinearities associated with regulation of the recycling apparatus.