Dust storms increase the tolerance of phytoplankton to thermal and pH changes.

Aquatic communities are increasingly subjected to multiple stressors through global change, including warming, pH shifts, and elevated nutrient concentrations. These stressors often surpass species tolerance range, leading to unpredictable consequences for aquatic communities and ecosystem functioning. Phytoplankton, as the foundation of the aquatic food web, play a crucial role in controlling water quality and the transfer of nutrients and energy to higher trophic levels. Despite the significance in understanding the effect of multiple stressors, further research is required to explore the combined impact of multiple stressors on phytoplankton. In this study, we used a combination of crossed experiment and mechanistic model to analyze the ecological and biogeochemical effects of global change on aquatic ecosystems and to forecast phytoplankton dynamics. We examined the effect of dust (0-75 mg L-1 ), temperature (19-27°C), and pH (6.3-7.3) on the growth rate of the algal species Scenedesmus obliquus. Furthermore, we carried out a geospatial analysis to identify regions of the planet where aquatic systems could be most affected by atmospheric dust deposition. Our mechanistic model and our empirical data show that dust exerts a positive effect on phytoplankton growth rate, broadening its thermal and pH tolerance range. Finally, our geospatial analysis identifies several high-risk areas including the highlands of the Tibetan Plateau, western United States, South America, central and southern Africa, central Australia as well as the Mediterranean region where dust-induced changes are expected to have the greatest impacts. Overall, our study shows that increasing dust storms associated with a more arid climate and land degradation can reverse the negative effects of high temperatures and low pH on phytoplankton growth, affecting the biogeochemistry of aquatic ecosystems and their role in the cycles of the elements and tolerance to global change.

A Model-Based 13C-Sucrose Breath Test Diagnostic for Gut Function Disorders Characterized by a Loss of Sucrase-Isomaltase Enzymatic Activity.

BACKGROUND: Environmental enteric dysfunction (EED) causes malnutrition in children in low-resource settings. Stable-isotope breath tests have been proposed as noninvasive tests of altered nutrient metabolism and absorption in EED, but uncertainty over interpreting the breath curves has limited their use. The activity of sucrose-isomaltase, the glucosidase enzyme responsible for sucrose hydrolysis, may be reduced in EED. We previously developed a mechanistic model describing the dynamics of the 13C-sucrose breath test (13C-SBT) as a function of underlying metabolic processes. OBJECTIVES: This study aimed to determine which breath test curve dynamics are associated with sucrose hydrolysis and with the transport and metabolism of the fructose and glucose moieties and to propose and evaluate a model-based diagnostic for the loss of activity of sucrase-isomaltase. METHODS: We applied the mechanistic model to 2 sets of exploratory 13C-SBT experiments in healthy adult participants. First, 19 participants received differently labeled sucrose tracers (U-13C fructose, U-13C glucose, and U-13C sucrose) in a crossover study. Second, 16 participants received a sucrose tracer accompanied by 0, 100, and 750 mg of Reducose, a sucrase-isomaltase inhibitor. We evaluated a model-based diagnostic distinguishing between inhibitor concentrations using receiver operator curves, comparing with conventional statistics. RESULTS: Sucrose hydrolysis and the transport and metabolism of the fructose and glucose moieties were reflected in the same mechanistic process. The model distinguishes these processes from the fraction of tracer exhaled and an exponential metabolic process. The model-based diagnostic performed as well as the conventional summary statistics in distinguishing between no and low inhibition [area under the curve (AUC): 0.77 vs. 0.66-0.79] and for low vs. high inhibition (AUC 0.92 vs. 0.91-0.99). CONCLUSIONS: Current summary approaches to interpreting 13C breath test curves may be limited to identifying only gross gut dysfunction. A mechanistic model-based approach improved interpretation of breath test curves characterizing sucrose metabolism.

Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation.

Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) toward sample variations. A possible structure of a feedforward model-based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model-based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high-performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during the DT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real-world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized.

Residence time distribution in continuous virus filtration.

Regulatory authorities recommend using residence time distribution (RTD) to address material traceability in continuous manufacturing. Continuous virus filtration is an essential but poorly understood step in biologics manufacturing in respect to fluid dynamics and scale-up. Here we describe a model that considers nonideal mixing and film resistance for RTD prediction in continuous virus filtration, and its experimental validation using the inert tracer NaNO3. The model was successfully calibrated through pulse injection experiments, yielding good agreement between model prediction and experiment ( R 2 > ${R}^{2}\gt $ 0.90). The model enabled the prediction of RTD with variations-for example, in injection volumes, flow rates, tracer concentrations, and filter surface areas-and was validated using stepwise experiments and combined stepwise and pulse injection experiments. All validation experiments achieved R 2 > ${R}^{2}\gt $ 0.97. Notably, if the process includes a porous material-such as a porous chromatography material, ultrafilter, or virus filter-it must be considered whether the molecule size affects the RTD, as tracers with different sizes may penetrate the pore space differently. Calibration of the model with NaNO3 enabled extrapolation to RTD of recombinant antibodies, which will promote significant savings in antibody consumption. This RTD model is ready for further application in end-to-end integrated continuous downstream processes, such as addressing material traceability during continuous virus filtration processes.

Differences in virus and immune dynamics for SARS-CoV-2 Delta and Omicron infections by age and vaccination histories.

Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data.We found that Omicron had higher R0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections.This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

Online monitoring of protein refolding in inclusion body processing using intrinsic fluorescence.

Inclusion bodies (IBs) are protein aggregates formed as a result of overexpression of recombinant protein in E. coli. The formation of IBs is a valuable strategy of recombinant protein production despite the need for additional processing steps, i.e., isolation, solubilization and refolding. Industrial process development of protein refolding is a labor-intensive task based largely on empirical approaches rather than knowledge-driven strategies. A prerequisite for knowledge-driven process development is a reliable monitoring strategy. This work explores the potential of intrinsic tryptophan and tyrosine fluorescence for real-time and in situ monitoring of protein refolding. In contrast to commonly established process analytical technology (PAT), this technique showed high sensitivity with reproducible measurements for protein concentrations down to 0.01 g L - 1 . The change of protein conformation during refolding is reflected as a shift in the position of the maxima of the tryptophan and tyrosine fluorescence spectra as well as change in the signal intensity. The shift in the peak position, expressed as average emission wavelength of a spectrum, was correlated to the amount of folding intermediates whereas the intensity integral correlates to the extent of aggregation. These correlations were implemented as an observation function into a mechanistic model. The versatility and transferability of the technique were demonstrated on the refolding of three different proteins with varying structural complexity. The technique was also successfully applied to detect the effect of additives and process mode on the refolding process efficiency. Thus, the methodology presented poses a generic and reliable PAT tool enabling real-time process monitoring of protein refolding.

Mechanistic Assessment of Cardiovascular State Informed by Vibroacoustic Sensors.

Monitoring blood pressure, a parameter closely related to cardiovascular activity, can help predict imminent cardiovascular events. In this paper, a novel method is proposed to customize an existing mechanistic model of the cardiovascular system through feature extraction from cardiopulmonary acoustic signals to estimate blood pressure using artificial intelligence. As various factors, such as drug consumption, can alter the biomechanical properties of the cardiovascular system, the proposed method seeks to personalize the mechanistic model using information extracted from vibroacoustic sensors. Simulation results for the proposed approach are evaluated by calculating the error in blood pressure estimates compared to ground truth arterial line measurements, with the results showing promise for this method.

How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models.

The unstructured mechanistic model (UMM) allows for modeling the macro-scale of a phenomenon without known mechanisms. This is extremely useful in biomanufacturing because using the UMM for the joint estimation of states and parameters with an extended Kalman filter (JEKF) can enable the real-time monitoring of bioprocesses with unknown mechanisms. However, the UMM commonly used in biomanufacturing contains ordinary differential equations (ODEs) with unshared parameters, weak variables, and weak terms. When such a UMM is coupled with an initial state error covariance matrix P(t=0) and a process error covariance matrix Q with uncorrelated elements, along with just one measured state variable, the joint extended Kalman filter (JEKF) fails to estimate the unshared parameters and state simultaneously. This is because the Kalman gain corresponding to the unshared parameter remains constant and equal to zero. In this work, we formally describe this failure case, present the proof of JEKF failure, and propose an approach called SANTO to side-step this failure case. The SANTO approach consists of adding a quantity to the state error covariance between the measured state variable and unshared parameter in the initial P(t = 0) of the matrix Ricatti differential equation to compute the predicted error covariance matrix of the state and prevent the Kalman gain from being zero. Our empirical evaluations using synthetic and real datasets reveal significant improvements: SANTO achieved a reduction in root-mean-square percentage error (RMSPE) of up to approximately 17% compared to the classical JEKF, indicating a substantial enhancement in estimation accuracy.

Towards good modelling practice for parallel hybrid models for wastewater treatment processes.

This study explores various approaches to formulating a parallel hybrid model (HM) for Water and Resource Recovery Facilities (WRRFs) merging a mechanistic and a data-driven model. In the study, the HM is constructed by training a neural network (NN) on the residual of the mechanistic model for effluent nitrate. In an initial experiment using the Benchmark Simulation Model no. 1, a parallel HM effectively addressed limitations in the mechanistic model's representation of autotrophic bacteria growth and the data-driven model's incapability to extrapolate. Next, different versions of a parallel HM of a large pilot-scale WRRF are constructed, using different calibration/training datasets and different versions of the mechanistic model to investigate the balance between the calibration effort for the mechanistic model and the compensation by the NN component. The HM can improve predictions compared to the mechanistic model. Training the NN on an independent validation dataset produced better results than on the calibration dataset. Interestingly, the best performance is achieved for the HM based on a mechanistic model using default (uncalibrated) parameters. Both long short-term memory (LSTM) and convolutional neural network (CNN) are tested as data-driven components, with a CNN HM (root-mean-squared error (RMSE) = 1.58 mg NO3-N/L) outperforming an LSTM HM (RMSE = 4.17 mg NO3-N/L).

Empirical methods for the validation of time-to-event mathematical models taking into account uncertainty and variability: application to EGFR + lung adenocarcinoma.

BACKGROUND: Over the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported. RESULTS: We highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use. CONCLUSIONS: With this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.

Ecological interactions driving population dynamics of two tick-borne pathogens, Borrelia burgdorferi and Babesia microti.

Borrelia burgdorferi (Bb) and Babesia microti (Bm) are vector-borne zoonotic pathogens commonly found co-circulating in Ixodes scapularis and Peromyscus leucopus populations. The restricted distribution and lower prevalence of Bm has been historically attributed to lower host-to-tick transmission efficiency and limited host ranges. We hypothesized that prevalence patterns are driven by coinfection dynamics and vertical transmission. We use a multi-year, multiple location, longitudinal dataset with mathematical modelling to elucidate coinfection dynamics between Bb and Bm in natural populations of P. leucopus, the most competent reservoir host for both pathogens in the eastern USA. Our analyses indicate that, in the absence of vertical transmission, Bb is viable at lower tick numbers than Bm. However, with vertical transmission, Bm is viable at lower tick numbers than Bb. Vertical transmission has a particularly strong effect on Bm prevalence early in the active season while coinfection has an increasing role during the nymphal peak. Our analyses indicate that coinfection processes, such as facilitation of Bm infection by Bb, have relatively little influence on the persistence of either parasite. We suggest future work examines the sensitivity of Bm vertical transmission and other key processes to local environmental conditions to inform surveillance and control of tick-borne pathogens.

PhenoCaB: a new phenological model based on carbon balance in boreal conifers.

Traditional phenological models use chilling and thermal forcing (temperature sum or degree-days) to predict budbreak. Because of the heightening impact of climate and other related biotic or abiotic stressors, a model with greater biological support is needed to better predict budbreak. Here, we present an original mechanistic model based on the physiological processes taking place before and during budbreak of conifers. As a general principle, we assume that phenology is driven by the carbon status of the plant, which is closely related to environmental variables and the annual cycle of dormancy-activity. The carbon balance of a branch was modelled from autumn to winter with cold acclimation and dormancy and from winter to spring when deacclimation and growth resumption occur. After being calibrated in a field experiment, the model was validated across a large area (> 34 000 km2 ), covering multiple conifers stands in Québec (Canada) and across heated plots for the SPRUCE experiment in Minnesota (USA). The model accurately predicted the observed dates of budbreak in both Québec (±3.98 d) and Minnesota (±7.98 d). The site-independent calibration provides interesting insights on the physiological mechanisms underlying the dynamics of dormancy break and the resumption of vegetative growth in spring.

Enhanced Thermal Stability and Reduced Aggregation in an Antibody Fab Fragment at Elevated Concentrations.

The aggregation of protein therapeutics such as antibodies remains a major challenge in the biopharmaceutical industry. The present study aimed to characterize the impact of the protein concentration on the mechanisms and potential pathways for aggregation, using the antibody Fab fragment A33 as the model protein. Aggregation kinetics were determined for 0.05 to 100 mg/mL Fab A33, at 65 °C. A surprising trend was observed whereby increasing the concentration decreased the relative aggregation rate, ln(v) (% day-1), from 8.5 at 0.05 mg/mL to 4.4 at 100 mg/mL. The absolute aggregation rate (mol L-1 h-1) increased with the concentration following a rate order of approximately 1 up to a concentration of 25 mg/mL. Above this concentration, there was a transition to an apparently negative rate order of -1.1 up to 100 mg/mL. Several potential mechanisms were examined as possible explanations. A greater apparent conformational stability at 100 mg/mL was observed from an increase in the thermal transition midpoint (Tm) by 7-9 °C, relative to those at 1-4 mg/mL. The associated change in unfolding entropy (△Svh) also increased by 14-18% at 25-100 mg/mL, relative to those at 1-4 mg/mL, indicating reduced conformational flexibility in the native ensemble. Addition of Tween or the crowding agents Ficoll and dextran, showed that neither surface adsorption, diffusion limitations nor simple volume crowding affected the aggregation rate. Fitting of kinetic data to a wide range of mechanistic models implied a reversible two-state conformational switch mechanism from aggregation-prone monomers (N*) into non-aggregating native forms (N) at higher concentrations. kD measurements from DLS data also suggested a weak self-attraction while remaining colloidally stable, consistent with macromolecular self-crowding within weakly associated reversible oligomers. Such a model is also consistent with compaction of the native ensemble observed through changes in Tm and △Svh.

Model-assisted analysis for tuning anthocyanin composition in grape berries.

Anthocyanin composition is responsible for the red colour of grape berries and wines, and contributes to their organoleptic quality. However, anthocyanin biosynthesis is under genetic, developmental and environmental regulation, making its targeted fine-tuning challenging. We constructed a mechanistic model to simulate the dynamics of anthocyanin composition throughout grape ripening in Vitis vinifera, employing a consensus anthocyanin biosynthesis pathway. The model was calibrated and validated using six datasets from eight cultivars and 37 growth conditions. Tuning the transformation and degradation parameters allowed us to accurately simulate the accumulation process of each individual anthocyanin under different environmental conditions. The model parameters were robust across environments for each genotype. The coefficients of determination (R2) for the simulated versus observed values for the six datasets ranged from 0.92 to 0.99, while the relative root mean square errors (RRMSEs) were between 16.8 and 42.1 %. The leave-one-out cross-validation for three datasets showed R2 values of 0.99, 0.96 and 0.91, and RRMSE values of 28.8, 32.9 and 26.4 %, respectively, suggesting a high prediction quality of the model. Model analysis showed that the anthocyanin profiles of diverse genotypes are relatively stable in response to parameter perturbations. Virtual experiments further suggested that targeted anthocyanin profiles may be reached by manipulating a minimum of three parameters, in a genotype-dependent manner. This model presents a promising methodology for characterizing the temporal progression of anthocyanin composition, while also offering a logical foundation for bioengineering endeavours focused on precisely adjusting the anthocyanin composition of grapes.

Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model.

The transmission rate is a central parameter in mathematical models of infectious disease. Its pivotal role in outbreak dynamics makes estimating the current transmission rate and uncovering its dependence on relevant covariates a core challenge in epidemiological research as well as public health policy evaluation. Here, we develop a method for flexibly inferring a time-varying transmission rate parameter, modeled as a function of covariates and a smooth Gaussian process (GP). The transmission rate model is further embedded in a hierarchy to allow information borrowing across parallel streams of regional incidence data. Crucially, the method makes use of optional vaccination data as a first step toward modeling of endemic infectious diseases. Computational techniques borrowed from the Bayesian spatial analysis literature enable fast and reliable posterior computation. Simulation studies reveal that the method recovers true covariate effects at nominal coverage levels. We analyze data from the COVID-19 pandemic and validate forecast intervals on held-out data. User-friendly software is provided to enable practitioners to easily deploy the method in public health research.

Impact of palmiped farm density on the resilience of the poultry sector to highly pathogenic avian influenza H5N8 in France.

We analysed the interplay between palmiped farm density and the vulnerability of the poultry production system to highly pathogenic avian influenza (HPAI) H5N8. To do so, we used a spatially-explicit transmission model, which was calibrated to reproduce the observed spatio-temporal distribution of outbreaks in France during the 2016-2017 epidemic of HPAI. Six scenarios were investigated, in which the density of palmiped farms was decreased in the municipalities with the highest palmiped farm density. For each of the six scenarios, we first calculated the spatial distribution of the basic reproduction number (R0), i.e. the expected number of farms a particular farm would be likely to infect, should all other farms be susceptible. We also ran in silico simulations of the adjusted model for each scenario to estimate epidemic sizes and time-varying effective reproduction numbers. We showed that reducing palmiped farm density in the densest municipalities decreased substantially the size of the areas with high R0 values (> 1.5). In silico simulations suggested that reducing palmiped farm density, even slightly, in the densest municipalities was expected to decrease substantially the number of affected poultry farms and therefore provide benefits to the poultry sector as a whole. However, they also suggest that it would not have been sufficient, even in combination with the intervention measures implemented during the 2016-2017 epidemic, to completely prevent the virus from spreading. Therefore, the effectiveness of alternative structural preventive approaches now needs to be assessed, including flock size reduction and targeted vaccination.

Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth.

Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then undergo a lag phase before resuming growth on the second. Biomass increase is characterised by a diauxic growth curve: exponential growth followed by a period of no growth before a second exponential growth. Recent literature lacks a complete dynamic description, artificially modelling lag phase and employing non-physical representations of precursor pools. Here, we formulate a rational mechanistic model based on flux-regulation/proteome partitioning with a finite precursor pool that reveals core mechanisms in a compact form. Unlike earlier systems, the characteristic dynamics emerge as part of the solution, including the lag phase. Focussing on growth of Escherichia coli on a glucose-lactose mixture we show results accurately reproduce experiments. We show that for a single strain of E. coli, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains.

Semi-mechanistic modeling of resistance development to ß-lactam and ß-lactamase-inhibitor combinations.

The use of ß-lactam (BL) and ß-lactamase inhibitor (BLI) combinations, such as piperacillin-tazobactam (PIP-TAZ) is an effective strategy to combat infections by extended-spectrum ß-lactamase-producing bacteria. However, in Gram-negative bacteria, resistance (both mutational and adaptive) to BL-BLI combination can still develop through multiple mechanisms. These mechanisms may include increased ß-lactamase activity, reduced drug influx, and increased drug efflux. Understanding the relative contribution of these mechanisms during resistance development helps identify the most impactful mechanism to target in designing a treatment to counter BL-BLI resistance. This study used semi-mechanistic mathematical modeling in combination with antibiotic sensitivity assays to assess the potential impact of different resistance mechanisms during the development of PIP-TAZ resistance in a Klebsiella pneumoniae isolate expressing CTX-M-15 and SHV-1 ß-lactamases. The mathematical models were used to evaluate the potential impact of several cellular changes as a sole mediator of PIP-TAZ resistance. Our semi-mechanistic model identified 2 out of the 13 inspected mechanisms as key resistance mechanisms that may independently support the observed magnitude of PIP-TAZ resistance, namely porin loss and efflux pump up-regulation. Simulation using the resulting models also suggested the possible adjustment of PIP-TAZ dose outside its commonly used 8:1 dosing ratio. The current study demonstrated how theory-based mechanistic models informed by experimental data can be used to support hypothesis generation regarding potential resistance mechanisms, which may guide subsequent experimental studies.

Pharmacometric model of agalsidase-migalastat interaction in human: a novel mechanistic model of drug-drug interaction between a therapeutic protein and a small molecule.

Recently, a new mechanism of drug-drug interaction (DDI) was reported between agalsidase, a therapeutic protein, and migalastat, a small molecule, both of which are treatment options of Fabry disease. Migalastat is a pharmacological chaperone that stabilizes the native form of both endogenous and exogenous agalsidase. In Fabry patients co-administrated with agalsidase and migalastat, the increase in active agalsidase exposure is considered a pharmacokinetic effect of agalsidase infusion but a pharmacodynamic effect of migalastat administration, which makes this new DDI mechanism even more interesting. To quantitatively characterize the interaction between agalsidase and migalastat in human, a pharmacometric DDI model was developed using literature reported concentration-time data. The final model includes three components: a 1-compartment linear model component for migalastat; a 2-compartment linear model component for agalsidase; and a DDI component where the agalsidase-migalastat complex is formed via second order association constant kon, dissociated with first order dissociation constant koff, and distributed/eliminated with same rates as agalsidase alone, albeit the complex (i.e., bound agalsidase) has higher enzyme activity compared to free agalsidase. The final model adequately captured several key features of the unique interaction between agalsidase and migalastat, and successfully characterized the kinetics of migalastat as well as the kinetics and activities of agalsidase when both drugs were used alone or in combination following different doses. Most parameters were reasonably estimated with good precision. Because the model includes mechanistic basis of therapeutic protein and small molecule pharmacological chaperone interaction, it can potentially serve as a foundational work for DDIs with similar mechanism.

The Applicability of Chromatographic Retention Modeling on Chiral Stationary Phases in Reverse-Phase Mode: A Case Study for Ezetimibe and Its Impurities.

Mechanistic modeling is useful for predicting and modulating selectivity even in early chromatographic method development. This approach is also in accordance with current analytical quality using design principles and is highly welcomed by the authorities. The aim of this study was to investigate the separation behavior of two different types of chiral stationary phases (CSPs) for the separation of ezetimibe and its related substances using the mechanistic retention modeling approach offered by the Drylab software (version 4.5) package. Based on the obtained results, both CSPs presented with chemoselectivity towards the impurities of ezetimibe. The cyclodextrin-based CSP displayed a higher separation capacity and was able to separate seven related substances from the active pharmaceutical ingredient, while the cellulose-based column enabled the baseline resolution of six impurities from ezetimibe. Generally, the accuracy of predicted retention times was lower for the polysaccharide CSP, which could indicate the presence of additional secondary interactions between the analytes and the CSP. It was also demonstrated that the combination of mechanistic modeling and an experimental design approach can be applied to method development on CSPs in reverse-phase mode. The applicability of the methods was tested on spiked artificial placebo samples, while intraday and long-term (2 years) method repeatability was also challenged through comparing the obtained retention times and resolution values. The results indicated the excellent robustness of the selected setpoints. Overall, our findings indicate that the chiral columns could offer orthogonal selectivity to traditional reverse-phase columns for the separation of structurally similar compounds.