Bruce A. Pearce

Human organ/tissue growth algorithms that include obese individuals and black/white population organ weight similarities from autopsy data.

Physiologically based pharmacokinetic (PBPK) models need the correct organ/tissue weights to match various total body weights in order to be applied to children and the obese individual. Baseline data from Reference Man for the growth of human organs (adrenals, brain, heart, kidneys, liver, lungs, pancreas, spleen, thymus, and thyroid) were augmented with autopsy data to extend the describing polynomials to include the morbidly obese individual (up to 250 kg). Additional literature data similarly extends the growth curves for blood volume, muscle, skin, and adipose tissue. Collectively these polynomials were used to calculate blood/organ/tissue weights for males and females from birth to 250 kg, which can be directly used to help parameterize PBPK models. In contrast to other black/white anthropomorphic measurements, the data demonstrated no observable or statistical difference in weights for any organ/tissue between individuals identified as black or white in the autopsy reports.

A computer model and program for xenobiotic disposition during pregnancy

A physiologically based pharmacokinetic computer model and program have been developed that depict internal disposition of chemicals during pregnancy in the mother and embryo/fetus. The model is based on human physiology but has been extended to simulate laboratory animal data. The model represents the distribution, metabolism, and elimination of two chemicals in both the maternal and embryo/fetal systems; the program handles the two chemicals completely independently or interactively with the two chemicals sharing routes of metabolism and/or elimination. The FORTRAN program computes the concentration of the two chemicals in 26 organs/tissues in the pregnant mother and 15 organs/tissues in the embryo/fetus using a 486DX4 or Pentium PC. Adjustments for embryo/fetal organ and tissue volumes as a function of developmental age are made utilizing the Gompertz growth equation for the developing embryo/fetus and allometric relationships for the developing organs. Various changes in the maternal compartments which could affect the distribution of a xenobiotic during pregnancy are also included in the model. Input files require estimates of binding coefficients, first- and/or second-order metabolism constants, level of interaction between the two chemicals, and dosing information. Different possible routes of administration are included (e.g., i.v., infusion, oral, dermal, and inhalation, as well as repeated doses or exposures). Regression analysis can be conducted on any combination of these various parameters to fit actual data. Output concentration-time curves are available simultaneously from all 82 differential equations. An illustrative example compares observed data with simulations for imipramine and its demethylated metabolite, desipramine, in both the maternal rat and her fetuses. Methyl mercury data for the non-pregnant and pregnant rat also are compared with human data. Based on parameters determined from analysis of rat data, the model is readjusted for human physiology and predicts human maternal and fetal tissue concentrations as a function of time.

BUILDING AN ORGAN-SPECIFIC CARCINOGENIC DATABASE FOR SAR ANALYSES

FDA reviewers need a means to rapidly predict organ-specific carcinogenicity to aid in evaluating new chemicals submitted for approval. This research addressed the building of a database to use in developing a predictive model for such an application based on structure–activity relationships (SAR). The Internet availability of the Carcinogenic Potency Database (CPDB) provided a solid foundation on which to base such a model. The addition of molecular structures to the CPDB provided the extra ingredient necessary for SAR analyses. However, the CPDB had to be compressed from a multirecord to a single record per chemical database; multiple records representing each gender, species, route of administration, and organ-specific toxicity had to be summarized into a single record for each study. Multiple studies on a single chemical had to be further reduced based on a hierarchical scheme. Structural cleanup involved removal of all chemicals that would impede the accurate generation of SAR type descriptors from commercial software programs; that is, inorganic chemicals, mixtures, and organometallics were removed. Counterions such as Na, K, sulfates, hydrates, and salts were also removed for structural consistency. Structural modification sometimes resulted in duplicate records that also had to be reduced to a single record based on the hierarchical scheme. The modified database containing 999 chemicals was evaluated for liver-specific carcinogenicity using a variety of analysis techniques. These preliminary analyses all yielded approximately the same results with an overall predictability of about 63%, which was comprised of a sensitivity of about 30% and a specificity of about 77%.

A FUSION OF ROUGH SETS, MODIFIED ROUGH SETS, AND GENETIC ALGORITHMS FOR HYBRID DIAGNOSTIC SYSTEMS

A hybrid classification system is a system composed of several intelligent techniques such that the inherent limitations of one individual technique be compensated for by the strengths of another technique. In this paper, we investigate the outline of a hybrid diagnostic system for Attention Deficit Disorder (ADD) in children. This system uses Rough Sets (RS) and Modified Rough Sets (MRS) to induce rules from examples and then uses our modified genetic algorithms to globalize the rules. Also, the classification capability of this hybrid system was compared with the behavior of (a) another hybrid classification system using RS, MRS, and the “dropping condition” approach, (b) the Interactive Dichotomizer 3 (ID3) approach, and (c) a basic genetic algorithm.

Windows ® based general PBPK/PD modeling software

A physiologically based pharmacokinetic (PBPK) model and program (called PostNatal) was developed which focuses on postnatal growth. Algorithms defining organ/tissue growth curves from birth through adulthood for male and female humans, dogs, rats, and mice are utilized to calculate the appropriate weight and blood flow for the internal organs/tissues. This Windows based program is actually four linked PBPK models with each PBPK model acting independently or totally integrated with the others through metabolism by first order or Michaelis-Menten kinetics. Data fitting is accomplished by a weighted least square regression algorithm. The model includes linkages for the simulation of pharmacodynamic (PD) effects.

Postnatal growth considerations for PBPK modeling.

A physiologically based pharmacokinetic (PBPK) model and Windows-based program (called PostNatal) was developed that focuses on postnatal growth, from birth through adulthood, using appropriate growth curves for each species and gender. Postnatal growth algorithms relating organs/tissues weights with total body weight for male and female humans, dogs, rats, and mice are an integral part of the software and are utilized to assign the appropriate weight and blood flow for each of 22 organs/tissues for each simulation. Upper limits of body weight were chosen that reflect the available data used to define the algorithms; above these limits a set percent body weight was assigned to all organs/tissues.

Improving proton MR spectroscopy of brain tissue for noninvasive diagnostics.

To examine preprocessing methods affecting the potential use of Magnetic Resonance Spectroscopy (MRS) as a noninvasive modality for detection and characterization of brain lesions and for directing therapy progress.

Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity

A diverse set of 154 chemicals that included US Food and Drug Administration-regulated compounds tested for their aquatic toxicity in Daphnia magna were modeled by a 3-dimensional quantitative spectral data-activity relationship (3D-QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity-based k-nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D-QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality-control criteria were imposed: 1) 100 randomized 20% hold-out test sets were generated and the average R(2) test of the respective models was used as a measure of their performance, and 2) a Y-scrambling procedure was used to identify chance correlations. A consensus between the best-performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average R(2) test  = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average R(2) test  = 0.801) offered an improvement of about 7.5% (R(2) test consensus  = 0.845). Projection of the most frequently occurring bins on the standard coordinate space indicated that the presence of a primary or secondary amino group-substituted aromatic systems-would result in an increased toxic effect in Daphnia. The presence of a second aromatic ring with highly electronegative substituents 5 Å to 7 Å apart from the first ring would lead to a further increase in toxicity.

Thymol treatment of bacteria prior to matrix-assisted laser desorption/ionization time-of-flight mass spectrometric analysis aids in identifying certain bacteria at the subspecies level.

RATIONALE The identification of bacteria based on mass spectra produced by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) has become routine since its introduction in 1996. The major drawback is that bacterial patterns produced by MALDI are dependent on sample preparation prior to analysis. This results in poor reproducibility in identifying bacterial types and between laboratories. The need for a more broadly applicable and useful sample handling procedure is warranted. METHODS Thymol was added to the suspension solvent of bacteria prior to MALDI analysis. The suspension solvent consisted of ethanol, water and TFA. The bacterium was added to the thymol suspension solvent and heated. An aliquot of the bacterial suspension was mixed directly with the matrix solution at a 9:1 ratio, matrix/bacteria solution, respectively. The mixture was then placed on the MALDI plate and allowed to air dry before MALDI analysis. RESULTS The thymol method improved the quality of spectra and number of peaks when compared to other sample preparation procedures studied. The bacterium-identifying biomarkers assigned to four strains of E. coli were statistically 95% reproducible analyzed on three separate days. The thymol method successfully differentiated between the four E. coli strains. In addition, the thymol procedure could identify nine out of ten S. enterica serovars over a 3-day period and nine S. Typhimurium strains from the other ten serovars 90% of the time over the same period. CONCLUSIONS The thymol method can identify certain bacteria at the sub-species level and yield reproducible results over time. It improves the quality of spectra by increasing the number of peaks when compared to the other sample preparation methods assessed in this study. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.

Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of Cytochrome P450 CYP3A4 and CYP2D6 Isozymes

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutant—interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D 13C and 1D 15N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D 13C-NMR and 15N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.