Use of computational fluid dynamics models for dosimetry of inhaled gases in the nasal passages

Computational fluid dynamics (CFD) models of the nasal passages of a rat, monkey, and human are being used (1) to determine important factors affecting nasal uptake, (2) to make interspecies dosimetric comparisons, (3) to provide detailed anatomical information for the rat, monkey, and human nasal passages, and (4) to provide estimates of regional air-phase mass transport coefficients (a measure of the resistance to gas transport from inhaled air to airway walls) in the nasal passages of all three species. For many inhaled materials, lesion location in the nose follows patterns that are both site and species specific. For reactive, water-soluble (Category 1) gases, regional uptake can be a major factor in determining lesion location. Since direct measurement of airflow and uptake is experimentally difficult, CFD models are used here to predict uptake patterns quantitatively in three-dimensional reconstructions of the F344 rat, rhesus monkey, and human nasal passages. In formaldehyde uptake simulations, absorption processes were assumed to be as rapid as possible, and regional flux (transport rate) of inhaled formaldehyde to airway walls was calculated for rats, primates, and humans. For uptake of gases like vinyl acetate and acrylic acid vapors, physiologically based pharmacokinetic uptake models incorporating anatomical and physical information from the CFD models were developed to estimate nasal tissue dose in animals and humans. The use of biologically based models in risk assessment makes sources of uncertainty explicit and, in doing so, allows quantification of uncertainty through sensitivity analyses. Limited resources can then be focused on reduction of important sources of uncertainty to make risk estimates more accurate.     

A computational fluid dynamics approach to assess interhuman variability in hydrogen sulfide nasal dosimetry

Human exposure to hydrogen sulfide (H₂S) gas occurs from natural and industrial sources and can result in dose-related neurological, respiratory, and cardiovascular effects. Olfactory neuronal loss in H₂S-exposed rats has been used to develop occupational and environmental exposure limits. Using nasal computational fluid dynamics (CFD) models, a correlation was found between wall mass flux and olfactory neuronal loss in rodents, suggesting an influence of airflow patterns on lesion locations that may affect interspecies extrapolation of inhaled dose. Human nasal anatomy varies considerably within a population, potentially affecting airflow patterns and dosimetry of inhaled gases. This study investigates interhuman variability of H₂S nasal dosimetry using anatomically accurate CFD models of the nasal passages of five adults and two children generated from magnetic resonance imaging (MRI) or computed tomography (CT) scan data. Using allometrically equivalent breathing rates, steady-state inspiratory airflow and H₂S uptake were simulated. Approximate locations of olfactory epithelium were mapped in each model to compare air:tissue flux in the olfactory region among individuals. The fraction of total airflow to the olfactory region ranged from 2% to 16%. Despite this wide range in olfactory airflow, H₂S dosimetry in the olfactory region was predicted to be similar among individuals. Differences in the 99th percentile and average flux values were <1.2-fold at inhaled concentrations of 1, 5, and 10 ppm. These preliminary results suggest that differences in nasal anatomy and ventilation among adults and children do not have a significant effect on H₂S dosimetry in the olfactory region.     

Use of a hybrid computational fluid dynamics and physiologically based inhalation model for interspecies dosimetry comparisons of ester vapors

Numerous inhalation studies have demonstrated that exposure to high concentrations of a wide range of volatile acids and esters results in cytotoxicity to the nasal olfactory epithelium. Previously, a hybrid computational fluid dynamics (CFD) and physiologically based pharmacokinetic (PBPK) dosimetry model was constructed to estimate the regional tissue dose of organic acids in the rodent and human nasal cavity. This study extends this methodology to a representative volatile organic ester, ethyl acrylate (EA). An in vitro exposure of explants of rat olfactory epithelium to EA with and without an esterase inhibitor demonstrated that the organic acid, acrylic acid, released by nasal esterases is primarily responsible for the olfactory cytotoxicity. Estimates of the steady-state concentration of acrylic acid in olfactory tissue were made for the rat nasal cavity by using data from a series of short-term in vivo studies and from the results of CFD-PBPK computer modeling. Appropriate parameterization of the CFD-PBPK model for the human nasal cavity and to accommodate human systemic anatomy, metabolism, and physiology allowed interspecies dose comparisons. The CFD-PBPK model simulations indicate that the olfactory epithelium of the human nasal cavity is exposed to at least 18-fold lower tissue concentrations of acid released from EA than the olfactory epithelium of the rat nasal cavity under the same exposure conditions. The magnitude of this difference varies with the specific exposure scenario that is simulated and with the specific dataset of human esterase activity used for the simulations. The increased olfactory tissue dose in rats relative to humans may be attributed to both the vulnerable location of the rodent olfactory tissue (comprising greater than 50% of the nasal cavity) and the high concentration of rat olfactory esterase activity (comparable to liver esterase activity) relative to human olfactory tissue. These studies suggest that the human olfactory epithelium is protected from vapors of organic esters significantly better than rat olfactory epithelium due to substantive differences in nasal anatomy, nasal and systemic metabolism, systemic physiology, and air flow. Although the accumulation of acrylic acid in the nasal tissues may be a primary concern for nasal irritation and human risk assessment, acute animal inhalation studies to evaluate lethality (LD50-type studies) conducted at very high vapor concentrations of ethyl acrylate indicated that a different mechanism is primarily responsible for mortality. The rodent studies demonstrated that systemic tissue nonprotein sulfhydryl depletion is a primary cause of death at exposure concentrations more than two orders of magnitude above the concentrations that induce nasal irritation. The CFD-PBPK model adequately simulated the severe depletion of glutathione in systemic tissues (e.g., liver and lung) associated with acute inhalation exposures in the 500-1000 ppm range. These results indicate that the CFD-PBPK model can simulate both the low-dose nasal tissue dosimetry associated with irritation and the high-dose systemic tissue dosimetry associated with mortality. In addition, the comparison of simulation results for ethyl acetate and acetone to nasal deposition data suggests that the CFD-PBPK model has general utility as a tool for dosimetry estimates for a wide range of other esters and slowly metabolized vapors.     

Ethyl acrylate risk assessment with a hybrid computational fluid dynamics and physiologically based nasal dosimetry model.

Cytotoxicity in the nasal epithelium is frequently observed in rodents exposed to volatile organic acids and esters by inhalation. An interspecies, hybrid computational fluid dynamics and physiologically based pharmacokinetic (CFD-PBPK) dosimetry model for inhaled ethyl acrylate (EA) is available for estimating internal dose measures for EA, its metabolite acrylic acid (AA), and EA-mediated reductions in tissue glutathione (GSH). Nasal tissue concentrations of AA were previously used as the dose metric for a chronic Reference Concentration (RfC) calculation with this compound. However, EA was more toxic than expected, based on calculated tissue AA concentrations. Unlike AA, EA causes depletion of tissue GSH. We have developed an RfC for EA using tissue GSH depletion in the olfactory epithelium as the primary measure of nasal tissue dose. The hybrid CFD-PBPK model was refined to improve the accuracy of simulations for GSH in rat olfactory tissues. This refined model was used to determine the concentration for continuous human exposures to EA predicted to reduce nasal GSH levels to the same extent as seen in rats exposed to EA at the no-observed-effect level (NOEL). Importantly, AA concentrations in the human nasal olfactory epithelium at the proposed chronic RfC were predicted to be lower than the AA concentrations estimated in the rat at the NOEL. Thus, a chronic RfC based on maintaining GSH in the human nasal olfactory epithelium at levels equivalent to the rat NOEL would also provide an adequate margin of safety with respect to AA concentrations in nasal tissues.     

Use of a pharmacokinetic-driven computational fluid dynamics model to predict nasal extraction of hydrogen sulfide in rats and humans.

Hydrogen sulfide (H2S) is a naturally occurring and industrially generated gas. Human exposure to H2S results in dose-related neurological, respiratory, and cardiovascular effects. Subchronic exposure of rats to 30 or 80 ppm H2S results in olfactory neuron loss and basal cell hyperplasia. Olfactory lesions commonly border main airflow streams in the rat, indicating an influence of airflow on H2S-induced lesion locations. In this study, anatomically accurate computational fluid dynamics (CFD) models were used to quantitatively predict H2S tissue dose in rat and human nasal passages. Air-tissue flux was defined in terms of H2S solubility, diffusivity, and reaction kinetics in nasal tissue. Kinetic parameters for the rat were estimated from an air-tissue pharmacokinetic (PK) model that was fit to experimental nasal extraction (NE) data. Using this PK-driven CFD model, predicted flux at the mid-dorsomedial meatus and the middle portion of the ethmoid recess showed a good correlation with olfactory lesion incidence. Scaled kinetic parameters were incorporated into a human CFD model to predict H2S flux in human nasal passages. Assuming that equivalent H2S flux values will induce similar responses in the olfactory regions of rats and humans, a no-observed-adverse-effect-level human-equivalent concentration was estimated to be 5 ppm. This estimate was based on quantitative tissue dose estimates extrapolated from both lesion and NE data in rats and represents a risk estimate that is science based and does not rely on simplified dosimetric assumptions for interspecies extrapolation.     

Incorporation of tissue reaction kinetics in a computational fluid dynamics model for nasal extraction of inhaled hydrogen sulfide in rats.

Rodents exposed to hydrogen sulfide (H2S) develop olfactory neuronal loss. This lesion has been used by the risk assessment community to develop occupational and environmental exposure standards. A correlation between lesion locations and areas of high H2S flux to airway walls has been previously demonstrated, but a quantitative dose assessment is needed to extrapolate dose at lesion sites to humans. In this study, nasal extraction (NE) of 10, 80, and 200 ppm H2S was measured in the isolated upper respiratory tract of anesthetized rats under constant unidirectional inspiratory flow rates of 75, 150, and 300 ml/min. NE was dependent on inspired H2S concentration and air flow rate: increased NE was observed when H2S exposure concentrations or inspiratory air flow rates were low. An anatomically accurate, three-dimensional computational fluid dynamics (CFD) model of rat nasal passages was used to predict NE of inhaled H2S. To account for the observed dependence of NE on H2S exposure concentration, the boundary condition used at airway walls incorporated first-order and saturable kinetics in nasal tissue to govern mass flux at the air:tissue interface. Since the kinetic parameters cannot be obtained using the CFD model, they were estimated independently by fitting a well-mixed, two-compartment pharmacokinetic (PK) model to the NE data. Predicted extraction values using this PK-motivated CFD approach were in good agreement with the experimental measurements. The CFD model provides estimates of localized H2S flux to airway walls and can be used to calibrate lesion sites by dose.     

Characterization of deposition from nasal spray devices using a computational fluid dynamics model of the human nasal passages.

Many studies suggest limited effectiveness of spray devices for nasal drug delivery due primarily to high deposition and clearance at the front of the nose. Here, nasal spray behavior was studied using experimental measurements and a computational fluid dynamics model of the human nasal passages constructed from magnetic resonance imaging scans of a healthy adult male. Eighteen commercially available nasal sprays were analyzed for spray characteristics using laser diffraction, high-speed video, and high-speed spark photography. Steadystate, inspiratory airflow (15 L/min) and particle transport were simulated under measured spray conditions. Simulated deposition efficiency and spray behavior were consistent with previous experimental studies, two of which used nasal replica molds based on this nasal geometry. Deposition fractions (numbers of deposited particles divided by the number released) of 20- and 50-microm particles exceeded 90% in the anterior part of the nose for most simulated conditions. Predicted particle penetration past the nasal valve improved when (1) the smaller of two particle sizes or the lower of two spray velocities was used, (2) the simulated nozzle was positioned 1.0 rather than 0.5 or 1.5 cm into the nostril, and (3) inspiratory airflow was present rather than absent. Simulations also predicted that delaying the appearance of normal inspiratory airflow more than 1 sec after the release of particles produced results equivalent to cases in which no inspiratory airflow was present. These predictions contribute to more effective design of drug delivery devices through a better understanding of the effects of nasal airflow and spray characteristics on particle transport in the nose.     

Computational fluid dynamics simulations of inhaled nano- and microparticle deposition in the rhesus monkey nasal passages

Abstract

Anatomically accurate computational fluid dynamics (CFD) models of the nasal passages of an infant (6 months old, 1.3?kg) and adult (7 years old, 11.9?kg) rhesus monkey were used to predict nasal deposition of inhaled nano- and microparticles. Steady-state, inspiratory airflow simulations were conducted at flow rates equal to 100, 200 and 300% of the estimated minute volume for resting breathing in each model. Particle transport and deposition simulations were conducted using the Lagrangian method to track the motion of inhaled particles. Nasal deposition fractions were higher in the infant model than the adult model at equivalent physiologic flow rates. Deposition curves collapsed when differences in nasal geometry were accounted for by plotting microparticle deposition versus the Stokes number and nanoparticle deposition as a function of the Schmidt number and diffusion parameter. Particle deposition was also quantified on major nasal epithelial types. Maximum olfactory deposition ranged from 5 to 14% for 1–2?nm particles in the adult and infant models, depending on flow rate. For these particle sizes, deposition on respiratory/transitional epithelia ranged from 40 to 50%. Increased deposition was also predicted for olfactory and respiratory/transitional epithelia for particle sizes >5?µm in the infant model and >8?µm in the adult model. Semi-empirical curves were developed based on the CFD simulation results to allow for simplified calculations of age-based deposition in the rhesus monkey nasal passages that can be implemented into lung dosimetry models.     

Selected responses of hypertension-sensitive and resistant rats to inhaled acrolein

The Dahl selected rat lines, one susceptible to salt-induced hypertension (DS) and the other resistant to salt-induced hypertension (DR), were exposed to filtered air, 0.4, 1.4, or 4.0 ppm acrolein for 6 h/day, 5 days/week for 62 days. All of the DS rats exposed to 4.0 ppm acrolein died within the first 11 days, while 60% of the DR animals survived the duration of the study. Neither dose dependent blood pressure changes nor altered behavioral characteristics were evident in either rat strain following acrolein exposure. Exposure to 4.0 ppm acrolein increased the level of several serum enzymes in the DR rats which survived. This concentration of acrolein also led to pulmonary edema and a significant increase in lung connective tissue in these animals. There was a marked difference in the pulmonary pathology observed in DS and DR rats exposed to 4.0 ppm acrolein. The lungs of moribund DS rats exhibited severe airway epithelial necrosis with edema and hemorrhage, while surviving DR rats primarily showed a proliferative change. Following exposure to 0.4 and 1.4 ppm acrolein, both rat lines displayed similar pathologic changes. Epithelial hyperplasia and/or clusters of macrophages were usually found near terminal bronchiolar areas. These findings suggest that further investigation of the physiopathologic sensitivity of the DS rat line may elucidate a model for investigating the underlying characteristics of stress susceptible populations.     

Analysis of particle deposition in the turbinate and olfactory regions using a human nasal computational fluid dynamics model.

The human nasal passages effectively filter particles from inhaled air. This prevents harmful pollutants from reaching susceptible pulmonary airways, but may leave the nasal mucosa vulnerable to potentially injurious effects from inhaled toxicants. This filtering property may also be strategically used for aerosolized nasal drug delivery. The nasal route has recently been considered as a means of delivering systemically acting drugs due to the large absorptive surface area available in close proximity to the nostrils. In this study, a computational fluid dynamics (CFD) model of nasal airflow was used with a particle transport and deposition code to predict localized deposition of inhaled particles in human nasal passages. The model geometry was formed from MRI scan tracings of the nasal passages of a healthy adult male. Spherical particles ranging in size from 5 to 50 microm were released from the nostrils. Particle trajectories and deposition sites were calculated in the presence of steady-state inspiratory airflow at volumetric flow rates of 7.5, 15, and 30 L/min. The nasal valve, turbinates, and olfactory region were defined in the CFD model so that particles depositing in these regions could be identified and correlated with their release positions on the nostril surfaces. When plotted against impaction parameter, deposition efficiencies in these regions exhibited maximum values of 53%, 20%, and 3%, respectively. Analysis of preferential deposition patterns and nostril release positions under natural breathing scenarios can be used to determine optimal particle size and flow rate combinations to selectively target drug particles to specific regions of the nose.     

Computer-aided design of dry powder inhalers using computational fluid dynamics to assess performance

Dry powder inhalers (DPIs) are gaining popularity for the delivery of drugs. A cost effective and efficient delivery device is necessary. Developing new DPIs by modifying an existing device may be the simplest way to improve the performance of the devices. The aim of this research was to produce a new DPIs using computational fluid dynamics (CFD). The new DPIs took advantages of the Cyclohaler® and the Rotahaler®. We chose a combination of the capsule chamber of the Cyclohaler® and the mouthpiece and grid of the Rotahaler®. Computer-aided design models of the devices were created and evaluated using CFD. Prototype models were created and tested with the DPI dispersion experiments. The proposed model 3 device had a high turbulence with a good degree of deagglomeration in the CFD and the experiment data. The %fine particle fraction (FPF) was around 50% at 60?L/min. The mass median aerodynamic diameter was around 2.8–4?μm. The FPF were strongly correlated to the CFD-predicted turbulence and the mechanical impaction parameters. The drug retention in the capsule was only 5–7%. In summary, a simple modification of the Cyclohaler® and Rotahaler® could produce a better performing inhaler using the CFD-assisted design.     

Characterization of regional and local deposition of inhaled aerosol drugs in the respiratory system by computational fluid and particle dynamics methods.

The present work describes the local deposition patterns of therapeutic aerosols in the oropharyngeal airways, healthy and diseased bronchi and alveoli using computational fluid and particle dynamics techniques. A user-enhanced computational fluid dynamics commercial finite- volume software package was used to compute airflow fields, deposition efficiencies, and deposition patterns of therapeutic aerosols along the airways. Adequate numerical meshes, generated in different airway sections, enabled us to more precisely define trajectories and local deposition patterns of inhaled particles than before. Deposition patterns show a high degree of heterogeneity of deposition along the airways, being more uniform for nanoparticles compared to micro-particles in the whole respiratory system at all inspiratory flow rates. Extrathoracic and tracheobronchial deposition fractions of nanoparticles decrease with increasing flow rates. However, vice versa happens to the micron-size particles, that is, the deposition fraction is higher at high flow rates. Both airway constrictions and the presence of tumors significantly increased the deposition efficiencies compared to the deposition efficiencies in healthy airways by a factor ranging from 1.2 to 4.4. In alveoli, the deposition patterns are strongly influenced by particle size and direction of gravity. This study demonstrated that numerical modeling can be a powerful tool in the aerosol drug delivery optimization. Present results may be integrated in future aerosol drug therapy protocols.     

Depletion of nasal mucosal glutathione by acrolein and enhancement of formaldehyde-induced DNA-protein cross-linking by simultaneous exposure to acrolein

Incubation of homogenates of rat nasal mucosa with acrolein resulted in the apparent formation of DNA-protein cross-links. However, inhalation exposure of male Fischer-344 rats to acrolein (2.0 ppm, 6 h) did not cause detectable DNA-protein cross-linking in the nasal respiratory mucosa. Simultaneous exposure of rats to both acrolein (2.0 ppm) and formaldehyde (6.0 ppm) for 6 h resulted in a significantly higher yield of DNA-protein cross-links than was obtained following exposure to formaldehyde (6.0 ppm) alone. Acrolein exposure at concentrations of 0.1, 0.5, 1.0, or 2.5 ppm resulted in a concentration-dependent depletion of nonprotein sulfhydryl groups in the nasal respiratory mucosa. A plausible explanation for the enhancement of DNA-protein cross-links by simultaneous exposure to formaldehyde and acrolein may be that depletion of glutathione by acrolein inhibited the oxidative metabolism of formaldehyde, leading to an increase of formaldehyde-induced DNA-protein cross-links.     

Understanding pharmacokinetics using realistic computational models of fluid dynamics: biosimulation of drug distribution within the CSF space for intrathecal drugs

We introduce how biophysical modeling in pharmaceutical research and development, combining physiological observations at the tissue, organ and system level with selected drug physiochemical properties, may contribute to a greater and non-intuitive understanding of drug pharmacokinetics and therapeutic design. Based on rich first-principle knowledge combined with experimental data at both conception and calibration stages, and leveraging our insights on disease processes and drug pharmacology, biophysical modeling may provide a novel and unique opportunity to interactively characterize detailed drug transport, distribution, and subsequent therapeutic effects. This innovative approach is exemplified through a three-dimensional (3D) computational fluid dynamics model of the spinal canal motivated by questions arising during pharmaceutical development of one molecular therapy for spinal cord injury. The model was based on actual geometry reconstructed from magnetic resonance imaging data subsequently transformed in a parametric 3D geometry and a corresponding finite-volume representation. With dynamics controlled by transient Navier–Stokes equations, the model was implemented in a commercial multi-physics software environment established in the automotive and aerospace industries. While predictions were performed in silico, the underlying biophysical models relied on multiple sources of experimental data and knowledge from scientific literature. The results have provided insights into the primary factors that can influence the intrathecal distribution of drug after lumbar administration. This example illustrates how the approach connects the causal chain underlying drug distribution, starting with the technical aspect of drug delivery systems, through physiology-driven drug transport, then eventually linking to tissue penetration, binding, residence, and ultimately clearance. Currently supporting our drug development projects with an improved understanding of systems physiology, biophysical models are being increasingly used to characterize drug transport and distribution in human tissues where pharmacokinetic measurements are difficult or impossible to perform. Importantly, biophysical models can describe emergent properties of a system, i.e. properties not identifiable through the study of the system’s components taken in isolation.     

Advances in computational methods to predict the biological activity of compounds

Importance of the field: The past decade had witnessed remarkable advances in computer science which had given rise to many new possibilities including the ability to simulate and model life's phenomena. Among one of the greatest gifts computer science had contributed to drug discovery is the ability to predict the biological activity of compounds and in doing so drives new prospects and possibilities for the development of novel drugs with robust properties. Areas covered in this review: This review presents an overview of the advances in the computational methods utilized for predicting the biological activity of compounds. What the reader will gain: The reader will gain a conceptual view of the quantitative structure-activity relationship paradigm and the methodological overview of commonly used machine learning algorithms. Take home message: Great advancements in computational methods have now made it possible to model the biological activity of compounds in an accurate manner. To obtain such a feat, it is often necessary to forgo several data pre-processing and post-processing procedures. A wide range of tools are available to perform such tasks; however, the proper selection and piecing together of complementary components in the prediction workflow remains a challenging and highly subjective task that heavily relies on the experience and judgment of the practitioner.     

Linking animal models of psychosis to computational models of dopamine function.

Psychosis is linked to dysregulation of the neuromodulator dopamine and antipsychotic drugs (APDs) work by blocking dopamine receptors. Dopamine-modulated disruption of latent inhibition (LI) and conditioned avoidance response (CAR) have served as standard animal models of psychosis and antipsychotic action, respectively. Meanwhile, the 'temporal difference' algorithm (TD) has emerged as the leading computational model of dopamine neuron firing. In this report TD is extended to include action at the level of dopamine receptors in order to explain a number of behavioral phenomena including the dose-dependent disruption of CAR by APDs, the temporal dissociation of the effects of APDs on receptors vs behavior, the facilitation of LI by APDs, and the disruption of LI by amphetamine. The model also predicts an APD-induced change to the latency profile of CAR--a novel prediction that is verified experimentally. The model's primary contribution is to link dopamine neuron firing, receptor manipulation, and behavior within a common formal framework that may offer insights into clinical observations.     

Advances in computational methods to predict the biological activity of compounds

Importance of the field: The past decade had witnessed remarkable advances in computer science which had given rise to many new possibilities including the ability to simulate and model life's phenomena. Among one of the greatest gifts computer science had contributed to drug discovery is the ability to predict the biological activity of compounds and in doing so drives new prospects and possibilities for the development of novel drugs with robust properties.

Areas covered in this review: This review presents an overview of the advances in the computational methods utilized for predicting the biological activity of compounds.

What the reader will gain: The reader will gain a conceptual view of the quantitative structure–activity relationship paradigm and the methodological overview of commonly used machine learning algorithms.

Take home message: Great advancements in computational methods have now made it possible to model the biological activity of compounds in an accurate manner. To obtain such a feat, it is often necessary to forgo several data pre-processing and post-processing procedures. A wide range of tools are available to perform such tasks; however, the proper selection and piecing together of complementary components in the prediction workflow remains a challenging and highly subjective task that heavily relies on the experience and judgment of the practitioner.     

The development of pharmacogenomic models to predict drug response

The potential of pharmacogenomic research to improve general healthcare, reduce morbidity and mortality resulting from treatment side effects, and to accelerate therapeutic compound discovery, design and development in the pharmaceutical industry, remains largely unfulfilled. Major contributory factors affecting progress in the field include: (i) the need for large clinical populations and control/placebo-treated cohorts to be studied; (ii) the difficulty in evaluating drug response in many instances with empirical or qualitative treatment response values often not available; (iii) interactions of underlying biochemical pathways are often not fully understood, both in terms of mode-of-action of a therapeutic compound itself and in the occurrence of side effects; (iv) population-specific frequency data for reported genetic variants and physically mapped genomic markers for evaluation in drug response studies are often not readily and/or publicly available; (v) accurate, high-throughput, cost-effective polymorphism detection and screening methods are required; and (vi) sophisticated biostatistical data analysis programs to perform the multi-parametric tests and permutation analyses necessary are still under development. With the recent human genome sequence draft release by both the public and commercial initiatives, in addition to the publication of locus and chromosome-specific polymorphism maps and TSC (The SNP Consortium) SNP map information expected in late-2001, it is hoped that at least some of the inherent difficulties in pharmacogenomics research will soon be alleviated.