Empirical Scaling Factor Research Articles

Improving the Translation of Organic Anion Transporting Polypeptide Substrates using HEK293 Cell Data in the Presence and Absence of Human Plasma via Physiologically Based Pharmacokinetic Modeling.

Accurately predicting the pharmacokinetics of compounds that are transporter substrates has been notoriously challenging using traditional in vitro systems and physiologically based pharmacokinetic (PBPK) modeling. The objective of this study was to use PBPK modeling to understand the translational accuracy of data generated with human embryonic kidney 293 (HEK293) cells overexpressing the hepatic uptake transporters organic anion transporting polypeptide (OATP) 1B1/3 with and without plasma while accounting for transporter expression. Models of four OATP substrates, two with low protein binding (pravastatin and rosuvastatin) and two with high protein binding (repaglinide and pitavastatin) were explored, and the OATP in vitro data generated in plasma incubations were used for a plasma model, and in buffer incubations for a buffer model. The pharmacokinetic parameters and concentration-time profiles of pravastatin and rosuvastatin were similar and well predicted (within 2-fold of observed values) using the plasma and buffer models without needing an empirical scaling factor, whereas the dispositions of the highly protein bound repaglinide and pitavastatin were more accurately simulated with the plasma models than the buffer models. This work suggests that data from HEK293 overexpressing transporter cells corrected for transporter expression represent a valid approach to improve bottom-up PBPK modeling for highly protein bound OATP substrates with plasma incubations and low protein binding OATP substrates with or without plasma incubations. SIGNIFICANCE STATEMENT: This work demonstrates the bottom-up approach of using in vitro data directly without employing empirical scaling factors to predict the intravenous pharmacokinetic (PK) profiles reasonably well for four organic anion transporting polypeptide (OATP) substrates. Based on these results, using HEK293 overexpressing cells, examining the impact of plasma for highly bound compounds, and incorporating transporter quantitation for the lot in which the in vitro data were generated represents a valid approach to achieve more accurate prospective PK predictions for OATP substrates.

Influence of Proteome Profiles and Intracellular Drug Exposure on Differences in CYP Activity in Donor-Matched Human Liver Microsomes and Hepatocytes.

Human liver microsomes (HLM) and human hepatocytes (HH) are important in vitro systems for studies of intrinsic drug clearance (CLint) in the liver. However, the CLint values are often in disagreement for these two systems. Here, we investigated these differences in a side-by-side comparison of drug metabolism in HLM and HH prepared from 15 matched donors. Protein expression and intracellular unbound drug concentration (Kpuu) effects on the CLint were investigated for five prototypical probe substrates (bupropion–CYP2B6, diclofenac–CYP2C9, omeprazole–CYP2C19, bufuralol–CYP2D6, and midazolam–CYP3A4). The samples were donor-matched to compensate for inter-individual variability but still showed systematic differences in CLint. Global proteomics analysis outlined differences in HLM from HH and homogenates of human liver (HL), indicating variable enrichment of ER-localized cytochrome P450 (CYP) enzymes in the HLM preparation. This suggests that the HLM may not equally and accurately capture metabolic capacity for all CYPs. Scaling CLint with CYP amounts and Kpuu could only partly explain the discordance in absolute values of CLint for the five substrates. Nevertheless, scaling with CYP amounts improved the agreement in rank order for the majority of the substrates. Other factors, such as contribution of additional enzymes and variability in the proportions of active and inactive CYP enzymes in HLM and HH, may have to be considered to avoid the use of empirical scaling factors for prediction of drug metabolism.

Prediction of Oral Pharmacokinetics Using a Combination of In Silico Descriptors and In Vitro ADME Properties.

Accurate prediction of oral pharmacokinetics remains challenging. This study investigated quantitative approaches for the prediction of the area under the plasma concentration-time curve after oral administration (AUCp,oral) to rats using the in vitro-in vivo extrapolation (IVIVE), in silico model using machine learning approaches and the combination of the in silico model and in vitro data. A set of 595 structurally diverse compounds with determined AUCp,oral at 1 mg/kg, in vitro intrinsic clearance (CLint), an unbound fraction in plasma (fu,p) in rats, and kinetic solubility at pH 6.8 was used for this assessment. Prediction models developed by two different types of machine learning techniques (i.e., random forest regression and Gaussian processes) were evaluated using three validation methods implementing the time and cluster-split training and test set and fivefold cross-validation. The developed machine learning models have a square of correlation coefficient (R2) in the range of 0.381-0.685 with 33-45% of the compounds being predicted within 2-fold of the observed AUCp,oral value. The predictivity was improved by incorporating CLint, fu,p, and solubility as explanatory variables with R2 = 0.554-0.743. In cases where extraction by the liver is the main elimination pathway and intestinal extraction is negligible, AUCp,oral can be expressed by dose, CLint, and fu,p based on a well-stirred model. By using this conventional IVIVE approach, only 1.7-5.0% of compounds were predicted within the 2-fold error with R2 = 0.354-0.487. Two empirical scaling factors (ESFs) determined by linear regression analysis and machine learning approaches improved the predictivity of AUCp,oral with 33-44% predicted within twofold variability. The IVIVE using ESF predicted by random forest regression showed better predictivity of AUCp,oral with R2 = 0.471-0.618, while it still showed lower predictivity than machine learning approaches applied directly to AUCp,oral prediction. This study demonstrated that the combination of in silico and in vitro parameters is useful to improve the predictivity of the machine learning model for rat AUCp,oral and supports consideration for predicting AUCp,oral for human and other non-clinical species in a similar manner.

Albumin-Mediated Uptake Improves Human Clearance Prediction for Hepatic Uptake Transporter Substrates Aiding a Mechanistic In Vitro-In Vivo Extrapolation (IVIVE) Strategy in Discovery Research.

This study focused on exploring various in vitro to in vivo extrapolation (IVIVE) approaches with the primary goal of improving human hepatic clearance (CL) prediction for OATP substrates. To that effect, the impact of albumin-mediated uptake in human hepatocytes was investigated. In vitro hepatic uptake assay using suspended human hepatocytes was performed with 16 selected OATP substrates to determine the uptake CL in the absence and presence of 4% BSA and unbound hepatocyte to media partition coefficient (Kpuu). Substantial enhancement of the unbound uptake CL (PSu,inf) was observed in the presence of 4% BSA, demonstrating "albumin-mediated" uptake. Prediction of human hepatic CL was performed using two non-traditional IVIVE approaches: initial uptake CL (PSu,inf) and intrinsic metabolic CL (CLint,met) corrected by Kpuu based on extended clearance concept. Compared to traditional IVIVE using CLint,met only, the two tested IVIVE approaches significantly improved the prediction of human hepatic CL. Particularly, direct extrapolation from PSu,inf (+BSA) showed the most robust correlation with in vivo human hepatic CL for all 16 compounds with bias of 1.9-2.0 for two lots of human hepatocytes, respectively. In addition, PSu,inf (+BSA) and Kpuu were also determined in suspended cynomolgus monkey hepatocytes. Prediction of monkey hepatic CL was improved by both approaches, although with more bias compared to human. These results suggested supplementing 4% BSA in human hepatocyte uptake assay provides a useful tool to characterize hepatic uptake CL for OATP substrates, enabling more accurate human CL prediction without any empirical scaling factor (ESF).

Proteomics-Informed Prediction of Rosuvastatin Plasma Profiles in Patients With a Wide Range of Body Weight.

Rosuvastatin is a frequently used probe to study transporter-mediated hepatic uptake. Pharmacokinetic models have therefore been developed to predict transporter impact on rosuvastatin disposition in vivo. However, the interindividual differences in transporter concentrations were not considered in these models, and the predicted transporter impact was compared with historical in vivo data. In this study, we investigated the influence of interindividual transporter concentrations on the hepatic uptake clearance of rosuvastatin in 54 patients covering a wide range of body weight. The 54 patients were given an oral dose of rosuvastatin the day before undergoing gastric bypass or cholecystectomy, and pharmacokinetic (PK) parameters were established from each patient's individual time-concentration profiles. Liver biopsies were sampled from each patient and their individual hepatic transporter concentrations were quantified. We combined the transporter concentrations with in vitro uptake kinetics determined in HEK293-transfected cells, and developed a semimechanistic model with a bottom-up approach to predict the plasma concentration profiles of the single dose of rosuvastatin in each patient. The predicted PK parameters were evaluated against the measured in vivo plasma PKs from the same 54 patients. The developed model predicted the rosuvastatin PKs within two-fold error for rosuvastatin area under the plasma concentration versus time curve (AUC; 78% of the patients; average fold error (AFE): 0.96), peak plasma concentration (Cmax ; 76%; AFE: 1.05), and terminal half-life (t1/2 ; 98%; AFE: 0.89), and captured differences in the rosuvastatin PKs in patients with the OATP1B1 521T<C polymorphism. This demonstrates that hepatic uptake clearance determined in transfected cell lines, together with proteomics scaling, provides a useful tool for prediction models, without the need for empirical scaling factors.

Comprehensive Assessment of GFN Tight-Binding and Composite Density Functional Theory Methods for Calculating Gas-Phase Infrared Spectra.

Vibrational spectroscopy is a valuable and widely used analytical tool for the characterization of chemical substances. We investigate the performance of semiempirical quantum mechanical GFN tight-binding and force-field methods for the calculation of gas-phase infrared spectra in comparison to experiment and low-cost (B3LYP-3c) density functional theory. A data set of 7247 experimental references was used to evaluate method performance based on automatic spectra comparison. Various quantitative spectral similarity measures were employed for the comparison between theory and experiment and for determining empirical scaling factors. It is shown that the scaling of atomic masses provides an accurate yet simple alternative to standard global frequency scaling in density functional theory (DFT) and semiempirical calculations. Furthermore, the method performance for 58 exemplary transition metal complexes was investigated. The efficient DFT composite method B3LYP-3c, that was introduced in the course of this work, was found to be excellently suited for general IR spectra calculations. The GFN1- and GFN2-xTB tight-binding methods clearly outperformed the PMx competitors. Conformational changes were investigated for a subset of the data and are found to have a mediocre strong influence on the simulated spectra suggesting that the corresponding elaborate sampling steps may be neglected in automated compound identification workflows.

A Laboratory-Specific Scaling Factor to Predict the In Vivo Human Clearance of Aldehyde Oxidase Substrates

Aldehyde oxidase (AO) efficiently metabolizes a range of compounds with N-containing heterocyclic aromatic rings and/or aldehydes. The limited knowledge of AO activity and abundance (in vitro and in vivo) has led to poor prediction of in vivo systemic clearance (CL) using in vitro-to-in vivo extrapolation approaches, which for drugs in development can lead to their discontinuation. We aimed to identify appropriate scaling factors to predict AO CL of future new chemical entities (NCEs). The metabolism of six AO substrates was measured in human liver cytosol (HLC) and S9 fractions. Measured blood-to-plasma ratios and free fractions (in the in vitro system and in plasma) were used to develop physiologically based pharmacokinetic models for each compound. The impact of extrahepatic metabolism was explored, and the intrinsic clearance required to recover in vivo profiles was estimated and compared with in vitro measurements. Using HLC data and assuming only hepatic metabolism, a systematic underprediction of clearance was observed (average fold underprediction was 3.8). Adding extrahepatic metabolism improved the accuracy of the results (average fold error of 1.9). A workflow for predicting metabolism of an NCE by AO is proposed, and an empirical (laboratory-specific) scaling factor of three on the predicted intravenous CL allows a reasonable prediction of the available clinical data. Alternatively, considering also extrahepatic metabolism, an scaling factor of 6.5 applied on the intrinsic clearance could be used. Future research should focus on the impact of the in vitro study designs and the contribution of extrahepatic metabolism to AO-mediated clearance to understand the mechanisms behind the systematic underprediction. SIGNIFICANCE STATEMENT: This works describes the development of scaling factors to allow in vitro-in vivo extrapolation of the clearance of compounds by aldehyde oxidase metabolism in humans. In addition, physiologically based pharmacokinetic models were developed for each of the aldehyde oxidase substrate compounds investigated.

Impact of pressure-based HACA rates on soot formation in varying-pressure coflow laminar diffusion flames

There are several processes to occur in soot formation and destruction for which some in the growth regime require a better understanding. In this work, a consistent surface reactivity model, developed in recent years, has been implemented across various sooting laminar flames at varying pressures. The surface reactivity function proposed by Khosousi and Dworkin (2015) is employed in the present study. It is based on the temperature history of soot particles. As the functionally dependent model has been derived and validated for atmospheric pressure flames, there are discrepancies between simulation and experiment that can be observed as pressures vary. One reason for these discrepancies could be explained by the fact that chemical reaction rates for the soot growth mechanism at atmospheric combustion do not adequately characterize the kinetics at higher pressures. Based on a recently published study (Frenklach et al., 2019), the elementary reaction rates that compose the Hydogen-Addition-Carbon-Abstraction soot surface growth mechanism depend on pressure and an empirical pressure scaling factor to account for this pressure dependence has been introduced. It has been determined that after applying the new empirical pressure scaling factor for the soot growth mechanism, the performance of the functionally dependent surface reactivity model improves in the wing regions of the flame for pure-ethylene flames; however, there is minimal change on the wings for the nitrogen-diluted flames. Additionally, the quantity for soot concentration along the centerline of all flames is nearly independent of the surface reactivity model chosen and needs further investigation. For the flames investigated, it is concluded that pressure dependent HACA rates do not alleviate all discrepancies between the numerical and experimental results and that further work is required.

Numerical modeling of solid-cluster evolution applied to the nanosecond solidification of water near the metastable limit.

Classical nucleation theory (CNT) is a promising way to predictively model the submicrosecond kinetics of phase transitions that occur under dynamic compression, such as the suite of experiments performed over the past two decades on the solidification of liquid water to the high-pressure ice VII phase. Myint et al. [Phys. Rev. Lett. 121, 155701 (2018)] presented the first CNT-based model for these types of rapid phase transitions, but relied on an empirical scaling parameter in their transient induction model to simulate the lag time that occurs prior to the onset of significant formation of ice VII clusters in the system. To build on that study, we model the liquid water-ice VII phase transformation using a numerical discretization scheme to solve the Zel'dovich-Frenkel partial differential equation, which is a fundamental CNT-based kinetic equation that describes the statistical time-dependent behavior of solid cluster formation. The Zel'dovich-Frenkel equation inherently accounts for transience in the nucleation kinetics and eliminates the need for the empirical scaling factor used by Myint et al. One major result of this research is that transience is found to play a relatively small role in the nucleation process for the dynamic-compression time scales of the liquid water-ice VII experiments being simulated. Instead, we show that it is possible to properly model the lag time using steady-state CNT by making small refinements to the interfacial free energy value. We have also developed a new dimensionless parameter that may be applied a priori to predict whether or not transient nucleation will be important in a given dynamic-compression experiment.

Combination of GRACE monthly gravity fields on the normal equation level

A large number of time-series of monthly gravity fields derived from GRACE data provide users with a wealth of information on mass transport processes in the system Earth. The users are, however, left alone with the decision which time-series to analyze. Following the example of other well-known combination services provided by the geodetic community, the prototype of a combination service has been developed within the frame of the project EGSIEM (2015–2017) to combine the different time-series with the goal to provide a unique and superior product to the user community. Four associated analysis centers (ACs) of EGSIEM, namely AIUB, GFZ, GRGS and IfG, generated monthly gravity fields which were then combined using the different normal equations (NEQs). But the relative weights determined by variance component estimation (VCE) on the NEQ level do not lead to an optimal combined product due to the different processing strategies applied by the individual ACs. We therefore resort to VCE on the solution level to derive relative weights that are representative of the noise levels of the individual solutions. These weights are then applied in the combination on the NEQ level. Prior to combination, empirical scaling factors that are based on pairwise combinations of NEQs are derived to balance the impact of the NEQs on the combined solution. We compare the processing approaches of the different ACs and introduce quality measures derived either from the differences w.r.t. the monthly means of the individual gravity fields or w.r.t. a deterministic signal model. After combination, the gravity fields are validated by comparison to the official GRACE SDS RL05 time-series and the individual contributions of the associated ACs in the spectral and the spatial domain. While the combined gravity fields are comparable in signal strength to the individual time-series, they stand out by their low noise level. In terms of noise, they are in 90% of all months as good or better than the best individual contribution from IfG and significantly less noisy than the official GRACE SDS RL05 time-series.

Polarizable Transferable Anisotropic United-Atom Force Field Based on the Mie Potential for Phase Equilibria: Ethers, n-Alkanes, and Nitrogen.

This study proposes a Polarizable Transferable Anisotropic Mie (P-TAMie) force field for ethers and mixtures of ethers with n-alkanes and nitrogen. In our approach, polarizability is accounted for using a charge-on-spring model (also referred to as the Drude model or core-shell model) placed on every united-atom site. Parameters for polarizabilities are obtained from ab initio calculations, without the need to introduce empirical scaling factors. The van der Waals parameters and static partial charges are optimized to minimize deviations to experimental vapor pressure and coexisting liquid density data. Vapor-liquid phase equilibria are calculated using Monte Carlo simulations in the grand canonical ensemble, using a MC scheme with multiparticle moves. Correlation results for pure substances and predictions of mixture behavior obtained from the proposed polarizable force field (P-TAMie) are in good agreement with experimental data. We compare the polarizable force field with an analogous nonpolarizable force field, namely, the Transferable Anisotropic Mie (TAMie) force field, where both force fields share the same intramolecular force field and were parametrized using the same objective function. The comparison should thus single out the role of polarizability in predicting phase equilibria. The comparison of P-TAMie with the nonpolarizable TAMie force field, however, shows very similar results of both models. This study does not include water and aqueous mixtures or other pronounced polar substances.

The dielectric constant: Reconciling simulation and experiment.

In this paper, we present a simple correction scheme to improve predictions of dielectric constants by classical non-polarisable models. This scheme takes into account electronic polarisation effects, through the experimental refractive index of the liquid, and a possible mismatch between the potential energy surface and the dipole moment surface. We have described the latter effect by an empirical scaling factor on the point charges, the value of which was determined by fitting the dielectric constant of methanol. Application of the same scaling factor to existing benchmark datasets, comprising four different models and a wide range of compounds, led to remarkable improvements in the quality of the predictions. In particular, the observed systematic underestimation of the dielectric constant was eliminated by accounting for the two missing terms in standard models. We propose that this correction term be included in future development and validation efforts of classical non-polarisable models.

In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.

A Novel Unified Approach to Predict Human Hepatic Clearance for Both Enzyme- and Transporter-Mediated Mechanisms Using Suspended Human Hepatocytes.

The accurate prediction of human pharmacokinetics is critically important in modern drug discovery since it drives both pharmacological and toxicological effects. Although significant progress has been made in predicting drug disposition by hepatic drug-metabolizing enzymes, predicting transporter-mediated clearance is still highly uncertain. Furthermore, different approaches are often used to predict clearance with and without transporter involvement, hence the major clearance pathway for a compound must first be determined to know which approach to use. As a result of these challenges, a novel unified method has been developed using cryopreserved suspended human hepatocytes to predict human hepatic clearance for both enzyme- and transporter-mediated mechanisms. This method hypothesizes that, once in vitro metabolic stability is scaled by partition coefficients between hepatocytes and buffer with 4% bovine serum albumin, in vivo clearance can be better predicted. With this method, good in vitro-in vivo correlation of human hepatic clearance has been obtained for a set of 32 structurally diverse compounds, including such transporters as organic anion-transporting polypeptide substrates. The clearance predictions for most compounds are within 3-fold of observed values. This is the first time that multiple compounds result in good in vitro-in vivo extrapolation using an entirely "bottom-up" approach without any empirical scaling factor when transporter-mediated clearance is involved. Potential exceptions are compounds with significant biliary and/or extra-hepatic clearance. The method offers an alternative approach to more accurately predict human hepatic clearance when multiple complex mechanisms are involved.

Use of Segregated Hepatocyte Scaling Factors and Cross-Species Relationships to Resolve Clearance Dependence in the Prediction of Human Hepatic Clearance.

Human and rat hepatocytes have a strong tendency to underpredict hepatic intrinsic clearance (CLint) and the extent of underprediction increases with increasing observed CLint In this study, application of the log average rat hepatocyte-rat in vivo empirical scaling factor (ESF) of 4.2 to human hepatocyte prediction successfully removed bias but did not improve precision. An analogous method using individual drug rat ESFs only achieved marginal improvement in accuracy but not precision. A novel approach to resolve clearance-dependent prediction, involving rat ESFs calculated for particular (order of magnitude) ranges of observed CLint (log average range, 0.12-2.1) improved human prediction precision but only modestly reduced bias. However, rat in vivo CLint was several-fold greater than human in vivo CLint and this was reflected in greater rat hepatocyte and microsome CLint, suggesting that rat metabolic enzymes are more efficient than their human counterparts, by several-fold. By applying the segregated rat ESFs followed by the human/rat CLint ratio, which was consistent regardless of CLint (log average 3.5), both accuracy and precision were improved, providing both a means of mitigating clearance dependence and reaffirming the potential role of rat hepatocytes for prediction of human metabolic CLint These cross-species observations indicate that underprediction from human in vitro systems may be predominantly consequential of an intrinsic property of the in vitro system rather than individual drug properties.

Hepatic Organic Anion Transporting Polypeptide-Mediated Clearance in the Beagle Dog: Assessing In Vitro-In Vivo Relationships and Applying Cross-Species Empirical Scaling Factors to Improve Prediction of Human Clearance.

In the present study, the beagle dog was evaluated as a preclinical model to investigate organic anion transporting polypeptide (OATP)-mediated hepatic clearance. In vitro studies were performed with nine OATP substrates in three lots of plated male dog hepatocytes ± OATP inhibitor cocktail to determine total uptake clearance (CLuptake) and total and unbound cell-to-medium concentration ratio (Kpuu). In vivo intrinsic hepatic clearances (CLint,H) were determined following intravenous drug administration (0.1 mg/kg) in male beagle dogs. The in vitro parameters were compared with those previously reported in plated human, monkey, and rat hepatocytes; the ability of cross-species scaling factors to improve prediction of human in vivo clearance was assessed. CLuptake in dog hepatocytes ranged from 9.4 to 135 µl/min/106 cells for fexofenadine and telmisartan, respectively. Active process contributed >75% to CLuptake for 5/9 drugs. Rosuvastatin and valsartan showed Kpuu > 10, whereas cerivastatin, pitavastatin, repaglinide, and telmisartan had Kpuu < 5. The extent of hepatocellular binding in dog was consistent with other preclinical species and humans. The bias (2.73-fold) obtained from comparison of predicted versus in vivo dog CLint,H was applied as an average empirical scaling factor (ESFav) for in vitro-in vivo extrapolation of human CLint,H The ESFav based on dog reduced underprediction of human CLint,H for the same data set (geometric mean fold error = 2.1), highlighting its utility as a preclinical model to investigate OATP-mediated uptake. The ESFav from all preclinical species resulted in comparable improvement of human clearance prediction, in contrast to drug-specific empirical scalars, rationalized by species differences in expression and/or relative contribution of particular transporters to drug hepatic uptake.

Towards an Accurate Prediction of Nitrogen Chemical Shifts by Density Functional Theory and Gauge‐Including Atomic Orbital

AbstractAn efficient, yet accurate, computational protocol for predicting nitrogen nuclear magnetic resonance (NMR) chemical shifts based on density functional theory and the gauge‐including atomic orbital approach is proposed. A database of small and relatively rigid compounds containing nitrogen atoms is compiled. Scaling factors for the linear correlation between experimental 15N chemical shifts and calculated isotropic shielding constants are systematically investigated with seven different levels of theory in both chloroform and dimethyl sulfoxide, two commonly used solvents for NMR experiments. The best method yields a root‐mean‐square deviation of about 5.30 and 7.00 ppm in CHCl3 and dimethyl sulfoxide (DMSO), respectively. Moreover, another set of scaling factors for –NH2 chemical shifts is also proposed based on a separate database with three levels of theory. Furthermore, it is encouraging that a reasonable transferability for the linear correlation is found between these two solvents. This finding will enable broader applications of the developed empirical scaling factors to other commonly used solvents in NMR experiments. The consistency between theoretical predictions and experimental results for structural elucidations is illustrated for selected examples including regioisomers, tautomers, oxidation states, and protonated structures.

A systematic benchmarking of computational vibrational spectroscopy with DFTB3: Normal mode analysis and fast Fourier transform dipole autocorrelation function.

Computational vibrational spectroscopy serves as an important tool in the interpretation of experimental infrared (IR) spectra. In this article, we present a systematic benchmarking study of DFTB3 with two different computational vibrational spectroscopic methods, based on either normal mode analysis (NMA) or fast Fourier transform dipole autocorrelation function (FT-DAC). The results were compared with experimental data and theoretical calculations with B3LYP/cc-pVTZ. The empirical scaling factors for DFTB3/NMA, DFTB3-freq/NMA, and DFTB3/FT-DAC methods are 0.9993, 1.0059, and 0.9982, respectively. We also demonstrate the significance of anharmonicity and conformational sampling in vibrational spectroscopic calculations on flexible molecules. As expected, DFTB3/FT-DAC predicted the anharmonic vibrational peaks more accurately than DFTB3/NMA and NMA spectra are highly dependent on the initial structures. The potential limitations of DFTB3 for vibrational spectroscopic calculations and the challenges in assigning the FT-DAC spectral peaks were noted. DFTB3/FT-DAC is expected to serve as a promising technique in computational spectroscopy in complex biomolecular systems. © 2018 Wiley Periodicals, Inc.

Mechanically adaptive thermoplastic polyurethane/cellulose nanocrystal composites: Process‐driven structure–property relationships

ABSTRACTTo enable filament extrusion additive manufacturing of mechanically adaptive nanocomposites, the effect of melt extrusion on the tensile modulus and mechanical adaptiveness of cellulose nanocrystal (CNC)/thermoplastic polyurethane (TPU) composites was investigated. TPU (Texin RxT70A) was processed with 10 wt % CNC in multiple formats, including solvent‐cast films and melt extrusion‐produced filaments. CNC orientation is characterized by polarized Raman spectroscopy and small‐angle X‐ray scattering (SAXS) and found to be uniaxially oriented in extruded filaments, and randomly oriented in solvent‐cast films. Dynamic mechanical analysis results show the addition of 10 wt % CNC increases the pure TPU storage modulus from 10 to 60–70 MPa in the dry state for solvent‐cast and extruded nanocomposites. Following water saturation, all CNC‐containing samples reach a similar wet storage modulus of approximately 26 MPa, regardless of CNC orientation and thermal history. Fickian diffusion scaling factors (6.8 to 11.6) scale closely to the time‐dependent empirical scaling factors (4.9 to 7.2), confirming the rate of mechanical adaptivity is driven by water diffusion through sample thickness. Our results suggest that mechanical adaptivity is retained following processing, enabling use as a feedstock for extrusion‐based additive manufacturing of CNC/TPU composites for mechanically adaptive parts. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 46992.

Development of a Dynamic Physiologically Based Mechanistic Kidney Model to Predict Renal Clearance.

Renal clearance is usually predicted via empirical approaches including quantitative structure activity relationship and allometric scaling. Recently, mechanistic prediction approaches using in silico kidney models have been proposed. However, empirical scaling factors are typically used to adjust for either passive diffusion or active secretion, to acceptably predict renal clearances. The goal of this study was to establish a renal clearance simulation tool that allows prediction of renal clearance (filtration and pH‐dependent passive reabsorption) from in vitro permeability data. A 35‐compartment physiologically based mechanistic kidney model was developed based on human physiology. The model was verified using 46 test compounds, including neutrals, acids, bases, and zwitterions. The feasibility of incorporating active secretion and pH‐dependent bidirectional passive diffusion into the model was demonstrated using para‐aminohippuric acid (PAH), cimetidine, memantine, and salicylic acid. The developed model enables simulation of renal clearance from in vitro permeability data, with predicted renal clearance within twofold of observed for 87% of the test drugs.