Quantitative Mechanistic Model Research Articles

Updating Reaction Mechanistic Domains for Skin Sensitization: 1. Nucleophilic Skin Sensitizers.

It has long been recognized that skin sensitizers either are electrophilic or can be activated to electrophilic species. Several nonanimal assays for skin sensitization are based on this premise. In the course of a project to update dermal sensitization thresholds (DST), we found a substantial number of sensitizers, with no electrophilic or pro-electrophilic alerts, that could be simply explained in terms of the sensitizer acting as a nucleophile. In some cases, the nucleophilic center is a sulfur or phosphorus atom, while in others, it is an aromatic carbon atom. For carbon-centered nucleophiles, a quantitative mechanistic model based on a combination of Hammett σ+ and logP values has been derived. This has been applied to rationalize several groups of known sensitizers with no electrophilic or pro-electrophilic alerts, including anacardic acids and cardols, which are known human sensitizers associated with, inter alia, cashew nut oil, mango, and Ginkgo biloba. The possibility of nucleophilic sensitization needs to be considered when evaluating new chemicals for skin sensitization potential and potency by nonanimal assays, particularly those based on the premise that skin sensitization is dependent upon reactions of electrophiles with skin protein-based nucleophiles.

Modeling temperature-dependent life-cycle toxicity of thiamethoxam in Chironomus riparius using a DEB-TKTD model

The neonicotinoid insecticide thiamethoxam (TMX) is widely used to protect crops against insect pests. Despite some desirable properties such as its low toxicity to birds and mammals, concerns have been raised about its toxicity to non-target arthropods, including freshwater insects like chironomids. Whereas multiple studies have investigated chronic effects of neonicotinoids in chironomid larvae at standardized laboratory conditions, a better understanding of their chronic toxicity under variable temperatures and exposure is needed for coherent extrapolation from the laboratory to the field. Here, we developed a quantitative mechanistic effect model for Chironomus riparius, to simulate the species’ life history under dynamic temperatures and exposure concentrations of TMX. Laboratory experiments at four different temperatures (12, 15, 20, 23 °C) and TMX concentrations between 4 and 51 µg/L were used to calibrate the model. Observed concentration-dependent effects of TMX in C. riparius included slower growth, later emergence, and higher mortality rates with increasing concentrations. Furthermore, besides a typical accelerating effect on the organisms’ growth and development, higher temperatures further increased the effects associated with TMX. With some data-informed modeling decisions, most prominently the inclusion of a size dependence that makes larger animals more sensitive to TMX, the model was parametrized to convincingly reproduce the data. Experiments at both a constant (20 °C) and a dynamically increasing temperature (15–23 °C) with pulsed exposure were used to validate the model. Finally, the model was used to simulate realistic exposure conditions using two reference exposure scenarios measured in Missouri and Nebraska, utilizing a moving time window (MTW) and either a constant temperature (20 °C) or the measured temperature profiles belonging to each respective scenario. Minimum exposure multiplication factors leading to a 10% effect (EP10) in the survival at pupation, i.e., the most sensitive endpoint found in this study, were 25.67 and 21.87 for the Missouri scenario and 38.58 and 44.64 for the Nebraska scenario, when using the respective temperature assumptions. While the results illustrate that the use of real temperature scenarios does not systematically modify the EPx in the same direction (making it either more or less conservative when used as a risk indicator), the advantage of this approach is that it increases the realism and thus reduces the uncertainty associated with the model predictions.

Quantitative mechanistic model reveals key determinants of placental IgG transfer and informs prenatal immunization strategies.

Transplacental antibody transfer is crucially important in shaping neonatal immunity. Recently, prenatal maternal immunization has been employed to boost pathogen-specific immunoglobulin G (IgG) transfer to the fetus. Multiple factors have been implicated in antibody transfer, but how these key regulators work together to elicit selective transfer is pertinent to engineering vaccines for mothers to optimally immunize their newborns. Here, we present the first quantitative mechanistic model to uncover the determinants of placental antibody transfer and inform personalized immunization approaches. We identified placental FcγRIIb expressed by endothelial cells as a limiting factor in receptor-mediated transfer, which plays a key role in promoting preferential transport of subclasses IgG1, IgG3, and IgG4, but not IgG2. Integrated computational modeling and in vitro experiments reveal that IgG subclass abundance, Fc receptor (FcR) binding affinity, and FcR abundance in syncytiotrophoblasts and endothelial cells contribute to inter-subclass competition and potentially inter- and intra-patient antibody transfer heterogeneity. We developed an in silico prenatal vaccine testbed by combining a computational model of maternal vaccination with this placental transfer model using the tetanus, diphtheria, and acellular pertussis (Tdap) vaccine as a case study. Model simulations unveiled precision prenatal immunization opportunities that account for a patient's anticipated gestational length, placental size, and FcR expression by modulating vaccine timing, dosage, and adjuvant. This computational approach provides new perspectives on the dynamics of maternal-fetal antibody transfer in humans and potential avenues to optimize prenatal vaccinations that promote neonatal immunity.

Mechanistic Multilayer Quantitative Model for Nonlinear Pharmacokinetics, Target Occupancy and Pharmacodynamics (PK/TO/PD) Relationship of D-Amino Acid Oxidase Inhibitor, TAK-831 in Mice

PurposeTAK-831 is a highly selective and potent inhibitor of D-amino acid oxidase (DAAO) currently under clinical development for schizophrenia. In this study, a mechanistic multilayer quantitative model that parsimoniously connects pharmacokinetics (PK), target occupancy (TO) and D-serine concentrations as a pharmacodynamic (PD) readout was established in mice.MethodsPK, TO and PD time-profiles were obtained in mice and analyzed by mechanistic binding kinetics model connected with an indirect response model in a step wise fashion. Brain distribution was investigated to elucidate a possible mechanism driving the hysteresis between PK and TO.ResultsThe observed nonlinear PK/TO/PD relationship was well captured by mechanistic modeling framework within a wide dose range of TAK-831 in mice. Remarkably different brain distribution was observed between target and reference regions, suggesting that the target-mediated slow binding kinetics rather than slow penetration through the blood brain barrier caused the observed distinct kinetics between PK and TO.ConclusionA quantitative mechanistic model for concentration- and time-dependent nonlinear PK/TO/PD relationship was established for TAK-831 in mice with accounting for possible rate-determining process. The established mechanistic modeling framework will provide a quantitative means for multilayer biomarker-assisted clinical development in multiple central nervous system indications.

Quantitative mechanistic model for ultrasmall nanoparticle-protein interactions.

To date, extensive effort has been devoted toward the characterization of protein interactions with synthetic nanostructures. However, much remains to be understood, particularly concerning microscopic mechanisms of interactions. Here, we have conducted a detailed investigation of the kinetics of nanoparticle-protein complexation to gain deeper insights into the elementary steps and molecular events along the pathway for complex formation. Toward that end, the binding kinetics between p-mercaptobenzoic acid-coated ultrasmall gold nanoparticles (AuMBA) and fluorescently-labeled ubiquitin was investigated at millisecond time resolution using stopped-flow spectroscopy. It was found that both the association and dissociation kinetics consisted of multiple exponential phases, hence suggesting a complex, multi-step reaction mechanism. The results fit into a picture where complexation proceeds through the formation of a weakly-bound first-encounter complex with an apparent binding affinity (KD) of ∼9 μM. Encounter complex formation is followed by unimolecular tightening steps of partial desolvation/ion removal and conformational rearrangement, which, collectively, achieve an almost 100-fold increase in affinity of the final bound state (apparent KD ∼0.1 μM). The final state is found to be weakly stabilized, displaying an average lifetime in the range of seconds. Screening of the electrostatic forces at high ionic strength weakens the AuMBA-ubiquitin interactions by destabilizing the encounter complex, whereas the average lifetime of the final bound state remains largely unchanged. Overall, our rapid kinetics investigation has revealed novel quantitative insights into the molecular-level mechanisms of ultrasmall nanoparticle-protein interactions.

Integrating spatiotemporal features of a ligand-regulated, multi-state allosteric protein.

SummaryDynamic protein molecules are defined by their spatiotemporal characteristics and should thus be represented by models incorporating both characteristics. Structural biology enables determination of atomic structures of individual conformational states of a given protein. Obtaining the complementary temporal information of a given time resolution, which can be directly linked to the corresponding atomic structures, requires identifying at each time point the specific conformational state adopted by the protein. Here, we examine individual RCK domains in the regulatory module of the MthK channel by monitoring in real time the orientation of an α-helix that is conformational-state-specific. The acquired dynamic information that specifies an RCK domain’s multi-state conformational changes, combined with already available corresponding atomic structures, enables us to establish an experiment-based spatiotemporal representation of an RCK domain, and to deduce a quantitative mechanistic model of the channel.

Hydraulic fracture-height containment by permeable weak bedding interfaces

In laminated formations, the vertical height growth of a hydraulic fracture can be strongly influenced by the interaction of the fracture tip with the bedding interfaces it crosses. A weak interface may fail in shear and then slip, depending on the strength and frictional properties, the effective vertical stress at the interface, and the net pressure. Shear failure and slippage at the interface can retard the height growth or even stop it completely. A 2D analytical model called the FracT model has been developed that examines the shear slippage along the bedding interface adjacent to the fracture tip and the resulting blunting of the fracture tip at the interface, as well as the stress condition on the face opposite from the hydraulic fracture tip for possible fracture nucleation that leads to fracture crossing. The growth of the shear slippage along the interface with time is coupled with the fluid flow into the permeable interface. A parametric study has been carried out to investigate the key formation parameters that influence the crossing/arrest of the fracture at the bedding interface and the shear slippage and depth of fluid penetration into the interface. The study suggests that the interfacial coefficient of friction and the ratio of the vertical to minimum horizontal stress are two of the most influential parameters governing fracture arrest by a weak interface. For the fracture tip to be arrested at the interface, the vertical stress acting on the interface must be close to the minimum horizontal stress or the interfacial coefficient of friction must be very small. The FracT model has also been integrated into a pseudo-3D-based complex hydraulic fracture model. This quantitative mechanistic model that incorporates a bedding-plane slip-driven mechanism is a necessary step to understand and bridge the characterization (sonic) and monitoring (microseismic) observations.

Skin Sensitization QMM for HRIPT NOEL Data: Aldehyde Schiff-Base Domain.

The general chemistry principles underlying skin sensitization for Schiff base (SB) electrophiles may be used to develop a quantitative mechanistic model (QMM), based on reactivity supplemented with a hydrophobicity parameter for some but not all structures within the SB reaction domain. For aliphatic Schiff base electrophiles, the log of the no observed effect level (NOEL) values (pNOEL) from the human repeated insult patch test (HRIPT) can be calculated by the reactivity parameter summation of sigma star values (Σσ*) and a hydrophobicity parameter (logP). Specifically, the QMM, pNOEL = 2.34(±0.33) Σσ* + 0.19(±0.07) logP - 2.62(±0.22), n = 19, R2 = 0.77, R2(adj) = 0.74, s = 0.20, F = 27, was developed. Not all parts of the Schiff base domain are modeled with one equation. Particularly, predicting aromatic aldehydes and ketones appears to require a separate equation. Interestingly, the same physical organic chemical properties originally applied to modeling the local lymph node assay potency of Schiff base electrophiles apply to human potency as represented by the HRIPT.

Interplay between microbial trait dynamics and population dynamics revealed by the combination of laboratory experiment and computational approaches

Filament formation is a common bacterial defense mechanism and possibly has a broad impact on microbial community dynamics. In order to examine the impact of filament formation on population dynamics, we developed an experimental system with a filamentous bacterium Flectobacillus sp. MWH38 and a ciliate predator Tetrahymena pyriformis. In this system, the effective defense of Flectobacillus resulted in the extinction of Tetrahymena by allowing almost no population growth. The result of a kairomone experiment suggested the existence of chemical signals for filament formation. To examine the mechanism further, we developed a quantitative mechanistic model and optimized the model for the experimental result using the simulated annealing method. We also performed a global parameter sensitivity analysis using an approximated Bayesian computation based on the sequential Monte Carlo method to reveal parameters to which the model behavior is sensitive to. Our model reproduced the population dynamics, as well as the cell size dynamics of Flectobacillus. The model behavior is sensitive to the nutrient uptake of Flectobacillus and the propensity of filament formation. It robustly predicts the extinction of Tetrahymena at the condition used in the experiment and predicts the transition from equilibrium to population cycle at higher nutrient conditions. Contrary to the previous study that disproved the presence of chemical signals for filament formation, our result suggested the importance of chemical signals at low predator density, suggesting the variety in bacterial resistance mechanisms that act at different stages of predator-prey interactions.

Binding of Solvent Molecules to a Protein Surface in Binary Mixtures Follows a Competitive Langmuir Model.

The binding of solvent molecules to a protein surface was modeled by molecular dynamics simulations of of Candida antarctica (C. antarctica) lipase B in binary mixtures of water, methanol, and toluene. Two models were analyzed: a competitive Langmuir model which assumes identical solvent binding sites with a different affinity toward water (KWat), methanol (KMet), and toluene (KTol) and a competitive Langmuir model with an additional interaction between free water and already bound water (KWatWat). The numbers of protein-bound molecules of both components of a binary mixture were determined for different compositions as a function of their thermodynamic activities in the bulk phase, and the binding constants were simultaneously fitted to the six binding curves (two components of three different mixtures). For both Langmuir models, the values of KWat, KMet, and KTol were highly correlated. The highest binding affinity was found for methanol, which was almost 4-fold higher than the binding affinities of water and toluene (KMet ≫ KWat ≈ KTol). Binding of water was dominated by the water-water interaction (KWatWat). Even for the three protein surface patches of highest water affinity, the binding affinity of methanol was 2-fold higher than water and 8-fold higher than toluene (KMet > KWat > KTol). The Langmuir model provides insights into the protein destabilizing mechanism of methanol which has a high binding affinity toward the protein surface. Thus, destabilizing solvents compete with intraprotein interactions and disrupt the tertiary structure. In contrast, benign solvents such as water or toluene have a low affinity toward the protein surface. Water is a special solvent: only few water molecules bind directly to the protein; most water molecules bind to already bound water molecules thus forming water patches. A quantitative mechanistic model of protein-solvent interactions that includes competition and miscibility of the components contributes a robust basis for solvent and protein engineering.

Two‐Dimensional Segmented Regression Model for Aluminum and Manganese Toxicity and Their Interaction in Soybean

The toxicity of Al and Mn frequently occurs together to limit crop growth in tropical acid soils. There is need to develop a quantitative mechanistic model to describe the toxicity of Al and Mn individually as well as their interaction. A two‐dimensional segmented regression model was developed to describe the plant's tolerance, linear response, and saturation effect of Al and Mn individually as a plateau‐linear‐plateau trend. The model also included the interaction between Al and Mn defined as the multiplication of their linear responses. Two published data sets to demonstrate the model were from a field liming experiment (soybean [Glycine max (L.) Merr.] grain yield changed with soil extractable Al and Mn) and a solution culture experiment (soybean root and shoot growth changed with solution Al and Mn). Results showed that the model fitted well the two data sets. Soybean yield and growth in both experiments were reduced by individual Al and Mn. In the liming experiment, the interaction between Al and Mn was not significant. In the solution culture experiment, there were significant interactions in amelioration of toxicity to both root and shoot growth. Besides the estimates of tolerant levels and saturation levels of Al and Mn in solution, two other criteria derived from the mathematical model were critical levels at 90% relative yield and characteristic levels when soybean responded to Al only and Mn only. The model and the derived criteria can be used for optimal soil liming to reduce Al and Mn toxicity and accurate screening of Al‐ and Mn‐tolerant genotypes. A new segmented regression model can quantify Al and Mn toxicity and interaction. Al and Mn interaction based on the model was more accurate than conventional ANOVA. Critical Al and Mn levels depended on each other as mathematical functions. Soybean did not respond to Al in solution culture when Mn was 50.741 μmol L−1.

Abstract P5-01-06: Characterization of metastatic breast cancer lesions with ferumoxytol MRI and treatment response to MM-398, nanoliposomal irinotecan (nal-IRI)

Abstract Introduction Irinotecan has known activity in metastatic breast cancer (MBC). MM-398, nanoliposomal irinotecan (nal-IRI), is designed to exploit leaky tumor vasculature for enhanced drug delivery to tumors. Tumor deposition of nal-IRI and subsequent conversion to SN-38 in both neoplastic cells and tumor associated macrophages (TAM) may positively correlate with activity. Predictive biomarkers to measure tumor deposition could identify patients likely to benefit from nal-IRI. Ferumoxytol (FMX), an iron-oxide superparamagnetic nanoparticle with MRI contrast properties, is taken up by TAMs with similar distribution patterns to nal-IRI in preclinical models. Our previous work has shown the feasibility of quantitative FMX MRI (Fe-MRI) of tumor lesions, and we developed a quantitative mechanistic PK model of FMX deposition (AACR 2014, abstract #CT224). Here we report nal-IRI activity and FMX levels in MBC patients on the study. Patients and methods Patients (n=15) with refractory solid tumors and at least two metastatic lesions >2 cm accessible for percutaneous biopsy were enrolled in a Phase 1 study. Fe-MRI scans were performed using T2* iron sensitive sequences prior to and following FMX infusion (1 h, 24 h, 72 h). T2* signal was used to calculate FMX levels in total lesions by comparison to a standard curve. Comparison of quantified FMX lesion uptake with a mechanistic PK model previously indicated that tissue permeability to FMX contributed to early Fe-MRI signals at 1 h and 24 h, while FMX binding contributed at 72 h. Patients then received nal-IRI (80 mg/m2 q2w) until progression. Core biopsies were obtained 72 h after both FMX and nal-IRI infusions. RECIST evaluation was done by CT every 8 weeks. Results FMX was well tolerated, and adverse events to nal-IRI were consistent with previous studies. Three of the 13 patients receiving nal-IRI had ER/PR+ MBC (median # of prior Rx: 8 compared to 4 for all study patients). Thirteen liver lesions (4-5/pt) were evaluated by FMX-MRI and CT for these 3 patients. Average lesion size: 26.9±11.2 mm diameter and 8.1±11.3 cm3 (median 4.7 cm3). Time on treatment for the 3 patients was 57, 126 and 256 days (study median 57 days). Best overall response was 1 stable disease (SD) and 1 partial response (PR) in these 3 patients. The patient with a PR had an average lesion size reduction of 44.5%, while the patient with SD had an average lesion size increase of 12.5% at final evaluation. Lesions that shrank after nal-IRI showed higher early levels of FMX compared to the study median (median 39.6 vs. 32.6 mcg/mL at 1 h; median 37.7 vs. 34.5 mcg/mL at 24 h). This relationship between lesion response and FMX levels was consistent with the lesion behavior in the full data set (n=31 lesions/9 patients across 7 indications) of the study. Conclusions Clinical activity of nal-IRI was observed in a subset of heavily treated ER/PR+ MBC patients. The relationship between FMX levels in tumor lesions and nal-IRI activity suggests that lesion permeability to FMX may be a useful biomarker for nal-IRI deposition and tumor response in MBC and potentially other indications. A multi-institution expansion of this study in HER2-negative MBC is planned to confirm these findings. Citation Format: Jasgit C Sachdev, Ramesh K Ramanathan, Natarajan Raghunand, Jaeyeon Kim, Stephan G Klinz, Eliel Bayever, Jonathan B Fitzgerald, Ronald L Korn. Characterization of metastatic breast cancer lesions with ferumoxytol MRI and treatment response to MM-398, nanoliposomal irinotecan (nal-IRI) [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P5-01-06.

Electrophilic Reactivity and Skin Sensitization Potency of SNAr Electrophiles

We published in 2011 a quantitative mechanistic model (QMM) for skin sensitization potency of SNAr electrophiles in the mouse local lymph node assay (LLNA). In this model, potency was correlated with a combination of σ* for the leaving group and the total σ(-) values of the other substituents in the aromatic ring. Shortly afterward Natsch et al. published a kinetic study in which rate constants were determined for reactions of SNAr electrophiles with the cysteine-based peptide Ac-RFAACAA (Cys-peptide) that is used in the direct peptide reactivity assay (DPRA), and correlations were sought between these rate constants and sensitization potency in the LLNA. These two publications together have enabled the present study, aiming to develop a linear free energy relationship (LFER) correlating Cys-peptide reactivity with a reactivity parameter (RP) based on a combination of σ* and σ(-) substituent constants and, by analyzing differences between the QMM based on RP and the QMM based on Cys-peptide rate constants, to gain further insights into the underlying chemistry of skin sensitization. For the 2,4-dinitro-X-subsituted benzenes (DNXB), the rate constants of Natsch et al. are well correlated with the reactivity parameter used in our earlier work, with two outliers. These are the compounds with X = F and X = SCN, which are both substantially more reactive toward Cys-peptide than predicted from comparison of their RP values with those of the other DNXB compounds. These two compounds are both negative outliers from a correlation of sensitization potency with experimental rate constants, but fit well to the correlation of sensitization potency with RP values. With these two compounds excluded, sensitization potency is well correlated with the experimental rate constants for the DNXB compounds (X = SO3(-), I, Br, Cl) together with 2,4-dichloro-1-nitrobenzene and 1,3,4,5-tetrachloro-2,6-dicyanobenzene. The regression equation is pEC3 = 0.88 log k + 4.03, R(2) = 0.966. The implication of DNFB being an outlier is that the model Cys-peptide nucleophile is substantially more sterically hindered than the cutaneous nucleophile(s) involved in the sensitization process. The pattern seen with 2,4-dinitrothiocyanatobenzene suggests that this compound reacts as an SNAr electrophile in the sensitization process, but by a different pathway, acting as a CN transfer agent, with the model Cys-peptide. For two further compounds, 2,4,6-trinitrochlorobenzene and 2,4,6-trinitrobenzenesulfonate, the Cys-peptide rate constants are well predicted by the reactivity parameter based on displacement of the Cl or SO3(-) substituent, but their sensitization potency is underestimated by both the Cys-peptide rate constant and this reactivity parameter. However, potency of these two compounds is well predicted by a reactivity parameter calculated on the basis of displacement of the 2-nitro group. This is interpreted as a case of sensitization being driven by the thermodynamically favored rather than the kinetically favored reaction product.

Assessing dose-dependent differences in DNA-damage, p53 response and genotoxicity for quercetin and curcumin

As part of a longer-term goal to create a quantitative mechanistic model of the p53-Mdm2 DNA-damage pathway, we are studying cellular responses to compounds causing DNA-damage by various modes-of action, including two natural polyphenols: quercetin (QUE) and curcumin (CUR). QUE and CUR are weak mutagens in some in vitro assays and possess both anti- or pro-oxidant effects depending on dose. This study examines the dose–response of DNA-damage pathway to these compounds in HT1080 cells (a human cell line with wild-type p53) at doses relevant to human exposure. CUR was more potent in causing reactive oxygen species, DNA damage (measured as phospho-H2AX) and p53 induction, with lowest observed effect levels (LOELs; 3–8μM) approximately three-fold lower than QUE (20–30μM). CUR showed a strong G2/M arrest and apoptosis at ∼10μM. QUE caused S phase arrest at low doses (8μM) and apoptosis was only induced at much higher doses (60μM). At concentrations with similar levels of p-H2AX and p53 biomarkers, CUR caused greater micronuclei frequency. CUR induced clear increases micronuclei at 3–6μM, while QUE had a weaker micronuclei response even at the highest doses. Thus, even with two compounds sharing common chemistries, DNA-damage response patterns differed significantly in terms of dose and cell fate.

A biophysically realistic computer model of Alzheimer pathology to guide the development of symptomatic drugs

The pharmaceutical industry is approaching unsustainable research costs to develop new drug therapies for mental disease because of the high failure rate in clinical trials. These failures are due to limitations of pre-clinical studies in animal models that fail to predict the efficacy of new drugs in human subjects. The gap between pre-clinical trials and clinical trials is particularly difficult in complex mental diseases such as Alzheimer’s disease because of the complex dynamics of the brain and the multiple chemical pathways that drugs can affect. However, many biological mechanisms associated with Alzheimer’s disease are now understood, and computational power and methods have reached the point for practical modeling of pathologies of Alzheimer’s disease. Numerical models can combine the information from animal studies of brain circuitry with data from human clinical trials of drug actions. Furthermore, complex interactions of multiple receptor targets can be predicted by a biophysical model of brain function. We introduce numerical models of neuronal microcircuitry that are associated with symptoms of Alzheimer’s disease. The emphasis is on the dynamics of these neural systems and how their dynamics are modified by therapeutic drugs. Unlike the current state-of-the-art methods of estimating therapeutic efficacy, the computational platform yields a significant increase in the predictive correlation with data from clinical trials. To develop a biophysical model for functional cognitive performance, we implemented a conductance-based computer model of multicompartment neuronal cell types in a cortical brain circuit for working memory using preclinical data on receptor pharmacology of catecholamine and cholinergic neurotransmitters. The pathology of Alzheimer’s disease was subsequently implemented as a loss of synapses and neurons and decreased cholinergic tone and the model as further calibrated the model with clinical ADAS-Cog results on acetylcholinesterase inhibitors and 5-HT6 antagonists. As an independent validation, we reproduce clinical data showing that ApoE genotype, implemented as a specific synapse loss and a decline in cholinergic tone, reduces the network performance much more in mild cognitive impairment conditions than at later stages of Alzheimer’s disease pathology. As a further example, we explore the reason for the differential effect of memantine, an NMDA subunit selective weak inhibitor in early and late Alzheimer’s disease pathology. The preferential inhibition of the NMDA-NR2C/NR2D subunits that are located on inhibitory interneurons compensates for the greater excitatory decline observed with stronger pathology. This quantitative systems pharmacology mechanistic disease model uses the full human pharmacology for clinical candidates in a humanized calibrated model of Alzheimer’s disease. Complementary to traditional animal models, the model has potential to assess the potential off-target effects of disease-modifying drugs, the consequences of pharmacologically active unique human metabolites, the effect of comedications and the impact of a small number of well-described genotypes.

Chemistry-Based Risk Assessment for Skin Sensitization: Quantitative Mechanistic Modeling for the SNAr Domain

There is a strong impetus to develop nonanimal based methods to predict skin sensitization potency. An approach based on physical organic chemistry, whereby chemicals are classified into reaction mechanistic domains and quantitative models or read-across methods are derived for each domain, has been the basis of several recent publications. This article is concerned with the S(N)Ar reaction mechanistic domain. Electrophiles able to react by the S(N)Ar mechanism have long been recognized as skin sensitizers and have been used extensively in research studies on the biology of skin sensitization. Although qualitative discriminant analysis approaches have been developed for estimating the sensitization potential for S(N)Ar electrophiles on a yes/no qualitative basis, no quantitative mechanistic model (QMM) has so far been developed for this domain. Here, we derive a QMM that correlates skin sensitization potency, quantified by murine local lymph node assay (LLNA) EC3 data on a range of S(N)Ar electrophiles. It is based on the Hammett σ(-) values for the activating groups and the Taft σ* value for the leaving group. The model takes the form pEC3=2.48 Σσ(-) + 0.60 σ* - 4.51. This QMM, generated from mouse LLNA data, provides a reactivity parameter 2.48 Σσ(-) + 0.60 σ*, which was applied to a set of 20 compounds for which guinea pig test results were available in the literature and was found to successfully discriminate the sensitizers from the nonsensitizers. The reactivity parameter correctly predicted a known human sensitizer 2,4-dichloropyrimidine. New LLNA data on two further S(N)Ar electrophiles are consistent with the QMM.

Does the extreme skin sensitization potency of benzoquinone result from special chemistry?

Benzoquinone is known to be an extreme skin sensitizer. Until now there has been no consideration of whether it owes its extreme skin sensitization potency to unique aspects of its chemistry or simply to its high reactivity as a Michael acceptor. To answer these questions, we have applied two mechanistic approaches to estimate a theoretical potency value for benzoquinone. A mechanistic read-across (MRA) approach, using known potency data for moderate sensitizers in the Michael acceptor domain, and a published quantitative mechanistic model (QMM) for skin sensitization potency of Michael acceptors, all in the moderate and weak range, were used. The read-across approach gives a theoretical local lymph node assay (LLNA) EC3 value of 0.04%, and the QMM approach gives a theoretical value of 0.013%, compared with an experimental value of 0.010%. The good agreement between the theoretical and experimental EC3 values supports the strength of the MRA and QMM approaches, and provides strong evidence that the extreme sensitization potency of benzoquinone can be attributed solely to its high reactivity as a Michael acceptor.

High Throughput Kinetic Profiling Approach for Covalent Binding to Peptides: Application to Skin Sensitization Potency of Michael Acceptor Electrophiles

Research aimed at nonanimal approaches to provide the relevant information needed for the effective assessment of skin sensitization, for both hazard characterization and risk assessment purposes, is currently an area of high activity, stimulated by regulatory initiatives related to chemicals used in consumer products. The ability of a chemical to react covalently with protein or peptide nucleophiles in the skin is recognized as the key determinant in determining sensitization potency, and initiatives to develop peptide reactivity assays to replace animal testing have been undertaken recently. This paper describes a high throughput kinetic profiling (HTKP) approach, developed as an extension of a published standard assay, with the aim of providing a quantitatively robust end point in the form of a kinetic profile from which reactivity to a model peptide can be quantified in the form of second order rate constants. The approach allows solubility issues to be identified and overcome; these are frequently encountered, but can often go undetected, in aqueous reactivity assays with organic compounds of interest in the skin sensitization context. Using rate constants determined by the HTKP approach we have obtained a quantitative mechanistic model for the Michael acceptor reaction mechanistic domain, relating the sensitization potency in the murine local lymph node assay to the rate constant. The observation that the correlation is not improved by incorporation of a hydrophobicity term has implications regarding the nature and location of the skin nucleophile whose reaction leads to sensitization by Michael acceptor electrophiles.

A Mechanistic Study of the Fischer–Tropsch Synthesis Using Transient Isotopic Tracing. Part 2: Model Quantification

A quantitative mechanistic model for the low-pressure Fischer–Tropsch synthesis reaction on a Co/Ru/TiO2 catalyst is presented. Although the Fischer–Tropsch synthesis is operated at dry conditions, the presence of a physisorbed state is essential in the mechanism. The monolayer coverage of the C3 to C20 hydrocarbons in the physisorbed state is low at 0.3%. The most abundant chemisorbed surface species are COads and two single-C species, Cα,ads and Cβ,ads. The fractional surface coverage of growing hydrocarbon chains is low at 1.4%. With increasing H2/CO feed ratio, the surface concentrations of Hads and free sites increase. The rate coefficient for chain initiation is one order of magnitude lower than that for chain growth. The rate coefficient of ethene readsorption is one order of magnitude higher than that for the readsorption of higher 1-olefins. Chain branching and bond shift are important secondary reactions at atmospheric pressure, transforming reactive 1-olefins into unreactive internal and isoolefins and thus decreasing the asymptotic chain growth probability. As is to be expected, the termination to paraffin is represented by a hydrogenation reaction. The termination to olefin, however, appears to be a desorption reaction rather than a hydrogenation or a dehydrogenation reaction. The growing hydrocarbon chain is therefore represented by a CiH2i species.

Kinetic study of polypropylene pyrolysis using ZSM-5 and an equilibrium fluid catalytic cracking catalyst

The effect of the addition of ZSM-5 zeolite and an equilibrium fluid catalytic cracking (FCC) catalyst in the pyrolysis of polypropylene has been studied, using dynamic thermogravimetry at a heating rate of 10 K min −1, with N 2 at 1 atm and using different contents of catalyst. Results obtained have shown the important effect of the presence of these catalysts—especially for the equilibrium FCC catalyst (E-cat)—in reducing the temperature of the complete degradation process as compared to the non-catalysed process. The saturating effect observed when using other catalysts such as MCM-41 was not observed when using E-cat or ZSM-5, which shows a linear tendency, with a higher decrease of decomposition temperature at higher catalyst loads. The differences between both catalysts indicate the important role of the selective matrix of the active alumina and other oxides of the FCC catalyst. A quantitative mechanistic kinetic model has been applied and the corresponding kinetic constants have been obtained by simultaneously correlating all the thermogravimetric experiments with different contents of catalyst. The kinetic parameters obtained have revealed a reduction in the activation energy of the catalytic decomposition as compared to the thermal process.