Pharmacokinetic/pharmacodynamic modeling in inflammation

Model-Based Virtual PK/PD Exploration and Machine Learning Approach to Define PK Drivers in Early Drug Discovery

While poor translatability of preclinical efficacy models can be responsible for clinical phase II failures, misdefinition of the optimal PK properties required to achieve therapeutic efficacy can also be a contributing factor. In the present work, the pharmacological dependency of PK end points in driving efficacy is demonstrated for six common pharmacological processes via model-based analysis. The analysis shows that the response is driven by multiple pharmacology-specific PK end points that change with how the response is defined. Moreover, the results demonstrate that the most important chemical structural features influencing response are specific to both target and downstream pharmacology, meaning the design and screening criteria must be defined uniquely for each target and corresponding pharmacology. The model-based virtual exploration of PK/PD relationships presented in this work offers one approach to identify target pharmacology-specific PK drivers and the associated potency-ADME space early in discovery to increase the probability of success and, ultimately, clinical attrition.

Differentiation of Neurons, Astrocytes, Oligodendrocytes and Microglia From Human Induced Pluripotent Stem Cells to Form Neural Tissue-On-Chip: A Neuroinflammation Model to Evaluate the Therapeutic Potential of Extracellular Vesicles Derived from Mesenchymal Stem Cells

Advances in stem cell (SC) technology allow the generation of cellular models that recapitulate the histological, molecular and physiological properties of humanized in vitro three dimensional (3D) models, as well as production of cell-derived therapeutics such as extracellular vesicles (EVs). Improvements in organ-on-chip platforms and human induced pluripotent stem cells (hiPSCs) derived neural/glial cells provide unprecedented systems for studying 3D personalized neural tissue modeling with easy setup and fast output. Here, we highlight the key points in differentiation procedures for neurons, astrocytes, oligodendrocytes and microglia from single origin hiPSCs. Additionally, we present a well-defined humanized neural tissue-on-chip model composed of differentiated cells with the same genetic backgrounds, as well as the therapeutic potential of bone marrow mesenchymal stem cells (BMSCs)-derived extracellular vesicles to propose a novel treatment for neuroinflammation derived diseases. Around 100 nm CD9 + EVs promote a more anti-inflammatory and pro-remodeling of cell-cell interaction cytokine responses on tumor necrosis factor-α (TNF-α) induced neuroinflammation in neural tissue-on-chip model which is ideal for modeling authentic neural-glial patho-physiology.

CYP2C9 Polymorphism Influence in PK/PD Model of Naproxen and 6-O-Desmethylnaproxen in Oral Fluid

Polymorphisms in CYP2C9 can significantly interfere with the pharmacokinetic (PK) and pharmacodynamic (PD) parameters of nonsteroidal anti-inflammatory drugs (NSAIDs), including naproxen. The present research aimed to study the PK/PD parameters of naproxen and its metabolite, 6-O-desmethylnaproxen, associated with allelic variations of CYP2C9. In our study, a rapid, selective, and sensitive Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) method was developed and validated for the determination of naproxen and its main metabolite, 6-O-desmethylnaproxen, in oral fluid. Naproxen and its main metabolite were separated using a Shim-Pack XR-ODS 75L × 2.0 column and C18 pre-column at 40 °C using a mixture of methanol and 10 mM ammonium acetate (70:30, v/v), with an injection flow of 0.3 mL/min. The total analytical run time was 3 min. The volunteers, previously genotyped for CYP2C9 (16 ancestral—CYP2C9 *1 and 12 with the presence of polymorphism—CYP2C9 *2 or *3), had their oral fluids collected sequentially before and after taking a naproxen tablet (500 mg) at the following times: 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5, 6 8, 11, 24, 48, 72 and 96 h. Significant differences in the PK parameters (* p < 0.05) of naproxen in the oral fluid were: Vd/F (L): 98.86 (55.58−322.07) and 380.22 (261.84−1097.99); Kel (1/h): 0.84 (0.69−1.34) and 1.86 (1.09−4.06), in ancestral and mutated CYP2C9 *2 and/or *3, respectively. For 6-O-desmethylnaproxen, no PK parameters were significantly different between groups. The analysis of prostaglandin E2 (PGE2) proved to be effective and sensitive for PD parameters analysis and showed higher levels in the mutated group (p < 0.05). Both naproxen and its main metabolite, 6-O-desmethylnaproxen, and PGE2 in oral fluid can be effectively quantified using LC-MS/MS after a 500 mg oral dose of naproxen. Our method proved to be effective and sensitive to determine the lower limit of quantification of naproxen and its metabolite, 6-O-desmethylnaproxen, in oral fluid (2.4 ng/mL). All validation data, such as accuracy, precision, and repeatability intra- and inter-assay, were less than 15%. Allelic variations of CYP2C9 may be considered relevant in the PK of naproxen and its main metabolite, 6-O-desmethylnaproxen.

Pyridine- and Thiazole-Based Hydrazides with Promising Anti-inflammatory and Antimicrobial Activities along with Their In Silico Studies

A new class of compounds formed by the linkage of -C(O)-NH- with pyridine and thiazole moieties was designed, synthesized, and characterized by various spectral approaches. The newly characterized compounds were evaluated for their antimicrobial as well as anti-inflammatory properties. The in vitro anti-inflammatory activity of these compounds was evaluated by denaturation of the bovine serum albumin method and showed inhibition in the range of IC50 values-46.29-100.60 μg/mL. Among all the tested compounds, compound 5l has the highest IC50 value and compound 5g has the least IC50 value. On the other hand, antimicrobial results revealed that compound 5j showed the lowest MIC values and compound 5a has the highest MIC values. Furthermore, molecular docking of the active compounds demonstrated a better docking score and interacted well with the target protein. Physicochemical parameters of the titled compounds were found suitable in the reference range only. The in silico molecular docking study revealed their COX-inhibitory action. Compound 5j emerged as a significant bioactive molecule among the synthesized analogues.

Curcumin analogs as the inhibitors of TLR4 pathway in inflammation and their drug like potentialities: a computer-based study

Toll-like receptor 4 (TLR4) pathway is one of the major pathways that mediate the inflammation in human body. There are different anti-inflammatory drugs available in the market which specifically act on different signaling proteins of TLR4 pathway but they do have few side effects and other limitations for intended use in human body. In this study, Curcumin and its different analogs have been analyzed as the inhibitors of signaling proteins, i.e. Cycloxygenase-2 (COX-2), inhibitor of kappaβ kinase (IKK) and TANK binding kinase-1 (TBK-1) of TLR4 pathway using different computational tools. Initially, three compounds were selected for respective target based on free binding energy among which different compounds were reported to have better binding affinity than commercially available drug (control). Upon continuous computational exploration with induced fit docking (IFD), 6-Gingerol, Yakuchinone A and Yakuchinone B were identified as the best inhibitors of COX-2, IKK, and TBK-1 respectively. Then their drug-like potentialities were analyzed in different experiments where they were also predicted to perform well. Hopefully, this study will uphold the efforts of researchers to identify anti-inflammatory drugs from natural sources.

Application of the relationship between pharmacokinetics and pharmacodynamics in drug development and therapeutic equivalence: a PEARRL review

Objectives

The objective of this review was to provide an overview of pharmacokinetic/pharmacodynamic (PK/PD) models, focusing on drug-specific PK/PD models and highlighting their value added in drug development and regulatory decision-making.

Key findings

Many PK/PD models, with varying degrees of complexity and physiological understanding have been developed to evaluate the safety and efficacy of drug products. In special populations (e.g. paediatrics), in cases where there is genetic polymorphism and in other instances where therapeutic outcomes are not well described solely by PK metrics, the implementation of PK/PD models is crucial to assure the desired clinical outcome. Since dissociation between the pharmacokinetic and pharmacodynamic profiles is often observed, it is proposed that physiologically based pharmacokinetic and PK/PD models be given more weight by regulatory authorities when assessing the therapeutic equivalence of drug products.

Summary

Modelling and simulation approaches already play an important role in drug development. While slowly moving away from ‘one-size fits all’ PK methodologies to assess therapeutic outcomes, further work is required to increase confidence in PK/PD models in translatability and prediction of various clinical scenarios to encourage more widespread implementation in regulatory decision-making.

PK/PD studies on non-selective PDE inhibitors in rats using cAMP as a marker of pharmacological response

In recent years, phosphodiesterase (PDE) inhibitors have been frequently tested for the treatment of experimental inflammatory and immune disorders. It is suggested that anti-inflammatory properties of PDE inhibitors are related to their ability to increase cAMP levels. The aim of this study was to verify the hypothesis that cAMP may be a useful marker of pharmacological response following administration of non-selective PDE inhibitors (pentoxifylline and (±)-lisofylline) to endotoxemic rats. Male Wistar rats were administered LPS (1 mg kg⁻¹, i.v.) simultaneously with either compound given at two doses (40 and 80 mg kg⁻¹, i.v.). Levels of cAMP and both compounds in animal plasma were measured by the validated HPLC methods. Pharmacokinetic-pharmacodynamic analysis was performed using basic and modified indirect response (IDR) models II in Phoenix WinNonlin. The results of this study indicate that, in contrast to pentoxifylline, (±)-lisofylline demonstrates a non-linear pharmacokinetics in rats with endotoxemia. In vitro study using human recombinant PDE4B and PDE7A revealed the occurrence of additive interaction between studied compounds. Moreover, (±)-lisofylline is a more potent inhibitor of PDEs compared to pentoxifylline, as evidenced by lower IC₅₀ values. Following administration of both compounds, levels of cAMP in rat plasma increased in a dose-dependent manner. The modified IDR model II better described cAMP levels over time profiles. The validity of the proposed marker was confirmed by measuring plasma TNF-α levels in the studied animals. In conclusion, cAMP may be used in future preclinical and clinical studies of some PDE inhibitors to evaluate the drug concentration–effect relationship.

Interrelationships between Infliximab and Recombinant Tumor Necrosis Factor-α in Plasma Using Minimal Physiologically Based Pharmacokinetic Models

The soluble cytokine tumor necrosis factor-α (TNF-α) is an important target for many therapeutic proteins used in the treatment of rheumatoid arthritis. Biologics targeting TNF-α exert their pharmacologic effects through binding and neutralizing this cytokine and preventing it from binding to its cell surface receptors. The magnitude of their pharmacologic effects directly corresponds to the extent and duration of free TNF-α suppression. However, endogenous TNF-α is of low abundance, so it is quite challenging to assess the free TNF-α suppression experimentally. Here we have applied an experimental approach to bypass this difficulty by giving recombinant human TNF-α (rhTNF-α) to rats by s.c. infusion. This boosted TNF-α concentration enabled quantification of TNF-α in plasma. Free rhTNF-α concentrations were measured after separation from the infliximab-rhTNF-α complex using Dynabeads Protein A. The interrelationship of infliximab and TNF-α was assessed with minimal physiologically based pharmacokinetic models for TNF-α and infliximab with a target-mediated drug disposition component. Knowledge of TNF-α pharmacokinetics allows reliable prediction of the free TNF-α suppression with either free or total TNF-α concentration profiles. The experimental and modeling approaches in our study may aid in the development of next-generation TNF-α inhibitors with improved therapeutic effects.

Systems pharmacology - Towards the modeling of network interactions

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior.

Methylation, Glucuronidation, and Sulfonation of Daphnetin in Human Hepatic Preparations In Vitro: Metabolic Profiling, Pathway Comparison, and Bioactivity Analysis

Our previous study demonstrated that daphnetin is subject to glucuronidation in vitro. However, daphnetin metabolism is still poorly documented. This study aimed to investigate daphnetin metabolism and its consequent effect on the bioactivity. Metabolic profiles obtained by human liver S9 fractions and human hepatocytes showed that daphnetin was metabolized by glucuronidation, sulfonation, and methylation to form 6 conjugates which were synthesized and identified as 7-O-glucuronide, 8-O-glucuronide, 7-O-sulfate and 8-O-sulfate, 8-O-methylate, and 7-O-suflo-8-O-methylate. Regioselective 8-O-methylation of daphnetin was investigated using in silico docking calculations, and the results suggested that a close proximity (2.03 Å) of 8-OH to the critical residue Lysine 144 might be the responsible mechanism. Compared with glucuronidation and sulfonation pathways, the methylation of daphnetin had a high clearance rate (470 μL/min/mg) in human liver S9 fractions and contributed to a large amount (37.3%) of the methyl-derived metabolites in human hepatocyte. Reaction phenotyping studies showed the major role of SULT1A1, -1A2, and -1A3 in daphnetin sulfonation, and soluble COMT in daphnetin 8-O-methylation. Of the metabolites, only 8-O-methyldaphnetin exhibited an inhibitory activity on lymphocyte proliferation comparable to that of daphnetin. In conclusion, methylation is a crucial pathway for daphnetin clearance and might be involved in pharmacologic actions of daphnetin in humans.

Pharmacokinetic and pharmacodynamic considerations for the next generation protein therapeutics

Increasingly sophisticated protein engineering efforts have been undertaken lately to generate protein therapeutics with desired properties. This has resulted in the discovery of the next generation of protein therapeutics, which include: engineered antibodies, immunoconjugates, bi/multi-specific proteins, antibody mimetic novel scaffolds, and engineered ligands/receptors. These novel protein therapeutics possess unique physicochemical properties and act via a unique mechanism-of-action, which collectively makes their pharmacokinetics (PK) and pharmacodynamics (PD) different than other established biological molecules. Consequently, in order to support the discovery and development of these next generation molecules, it becomes important to understand the determinants controlling their PK/PD. This review discusses the determinants that a PK/PD scientist should consider during the design and development of next generation protein therapeutics. In addition, the role of systems PK/PD models in enabling rational development of the next generation protein therapeutics is emphasized.

Computational Studies of the Intestinal Host-Microbiota Interactome

A large and growing body of research implicates aberrant immune response and compositional shifts of the intestinal microbiota in the pathogenesis of many intestinal disorders. The molecular and physical interaction between the host and the microbiota, known as the host-microbiota interactome, is one of the key drivers in the pathophysiology of many of these disorders. This host-microbiota interactome is a set of dynamic and complex processes, and needs to be treated as a distinct entity and subject for study. Disentangling this complex web of interactions will require novel approaches, using a combination of data-driven bioinformatics with knowledge-driven computational modeling. This review describes the computational approaches for investigating the host-microbiota interactome, with emphasis on the human intestinal tract and innate immunity, and highlights open challenges and existing gaps in the computation methodology for advancing our knowledge about this important facet of human health.

Modeling and design of challenge tests: Inflammatory and metabolic biomarker study examples

Given the complexity of pharmacological challenge experiments, it is perhaps not surprising that design and analysis, and in turn interpretation and communication of results from a quantitative point of view, is often suboptimal. Here we report an inventory of common designs sampled from anti-inflammatory, respiratory and metabolic disease drug discovery studies, all of which are based on animal models of disease involving pharmacological and/or patho/physiological interaction challenges. The corresponding data are modeled and analyzed quantitatively, the merits of the respective approach discussed and inferences made with respect to future design improvements. Although our analysis is limited to these disease model examples, the challenge approach is generally applicable to the vast majority of pharmacological intervention studies. In the present five Case Studies results from pharmacodynamic effect models from different therapeutic areas were explored and analyzed according to five typical designs. Plasma exposures of test compounds were assayed by either liquid chromatography/mass spectrometry or ligand binding assays. To describe how drug intervention can regulate diverse processes, turnover models of test compound-challenger interaction, transduction processes, and biophase time courses were applied for biomarker response in eosinophil count, IL6 response, paw-swelling, TNFα response and glucose turnover in vivo. Case Study 1 shows results from intratracheal administration of Sephadex, which is a glucocorticoid-sensitive model of airway inflammation in rats. Eosinophils in bronchoalveolar fluid were obtained at different time points via destructive sampling and then regressed by the mixed-effects modeling. A biophase function of the Sephadex time course was inferred from the modeled eosinophil time courses. In Case Study 2, a mouse model showed that the time course of cytokine-induced IL1β challenge was altered with or without drug intervention. Anakinra reversed the IL1β induced cytokine IL6 response in a dose-dependent manner. This Case Study contained time courses of test compound (drug), challenger (IL1β) and cytokine response (IL6), which resulted in high parameter precision. Case Study 3 illustrates collagen-induced arthritis progression in the rat. Swelling scores (based on severity of hind paw swelling) were used to describe arthritis progression after the challenge and the inhibitory effect of two doses of an orally administered test compound. In Case Study 4, a cynomolgus monkey model for lipopolysaccharide LPS-induced TNFα synthesis and/or release was investigated. This model provides integrated information on pharmacokinetics and in vivo potency of the test compounds. Case Study 5 contains data from an oral glucose tolerance test in rats, where the challenger is the same as the pharmacodynamic response biomarker (glucose). It is therefore convenient to model the extra input of glucose simultaneously with baseline data and during intervention of a glucose-lowering compound at different dose levels. Typically time-series analyses of challenger- and biomarker-time data are necessary if an accurate and precise estimate of the pharmacodynamic properties of a test compound is sought. Erosion of data, resulting in the single-point assessment of drug action after a challenge test, should generally be avoided. This is particularly relevant for situations where one expects time-curve shifts, tolerance/rebound, impact of disease, or hormetic concentration-response relationships to occur.

Controlled release of thymosin beta 4 using a collagen-chitosan sponge scaffold augments cutaneous wound healing and increases angiogenesis in diabetic rats with hindlimb ischemia

It is important to establish an efficient vascularization for the long-term acceptance of bioengineered skin equivalents treating the cutaneous wounds of diabetic rats with hindlimb ischemia. This study investigates the possible use of a collagen-chitosan sponge scaffold encapsulated with thymosin beta 4 (CCSS-eTβ4), an angiogenic factor, to accelerate cutaneous wound healing in streptozotocin (STZ)-induced diabetic rats with hindlimb ischemia. CCSSs-eTβ4 was fabricated using a freeze-drying method. The scaffolds were analyzed by scanning electron microscopy, swelling and degradation assays, mechanical properties, and scaffolds of 50:50 collagen-chitosan were selected and applied. The controlled release of Tβ4 from the scaffolds elicited localized and prolonged effects over 12 days, as shown by an enzyme-linked immunosorbent assay (ELISA). In vivo, CCSSs-eTβ4 improved diabetic cutaneous wound healing, with faster wound reepithelialization, better dermal reorganization, and higher wound vascularization. Furthermore, CCSSs-eTβ4 downregulated inflammatory genes and upregulated angiogenic genes in the wound tissue. Significant increases in CD31-positive endothelial cells and new vessel density were also observed. In vitro, Tβ4 increased the migratory and proliferative activity of high glucose (HG)-treated human umbilical vein endothelial cells (HUVECs). Meanwhile, we found that Tβ4 could promote HG-treated HUVECs migration and improve angiogenesis by activation of the VEGF/AKT pathway. Overall, these findings demonstrated the promising potential of CCSSs-eTβ4 to promote more effective wound healing and suggest its possible application for diabetic cutaneous wound treatment.

Modeling pharmacokinetics/pharmacodynamics of abatacept and disease progression in collagen-induced arthritic rats: a population approach

The PK/PD of abatacept, a selective T cell co-stimulation modulator, was examined in rats with collagen-induced arthritis (CIA) using a nonlinear mixed effect modeling approach. Male Lewis rats underwent collagen induction to produce rheumatoid arthritis. Two single-dose groups received either 10 mg/kg intravenous (IV) or 20 mg/kg subcutaneous (SC) abatacept, and one multiple-dose group received one 20 mg/kg SC abatacept dose and four additional 10 mg/kg SC doses. Effects on disease progression (DIS) were measured by paw swelling. Plasma concentrations of abatacept were assayed by enzyme-linked immunosorbent assay. The PK/PD data were sequentially fitted using NONMEM VI. Goodness-of-fit was assessed by objective functions and visual inspection of diagnostic plots. The PK of abatacept followed a two-compartment model with linear elimination. For SC doses, short-term zero-order absorption was assumed with F = 59.2 %. The disease progression component was an indirect response model with a time-dependent change in paw edema production rate constant (k in ) that was inhibited by abatacept. Variation in the PK data could be explained by inter-individual variability in clearance and central compartment volume (V 1 ), while the large variability of the PD data may be the result of paw edema production (k in 0 ) and loss rate constant (k out ). Abatacept has modest effects on paw swelling in CIA rats. The PK/PD profiles were well described by the proposed model and allowed evaluation of inter-individual variability on drug- and DIS-related parameters.

Interindividual variability in hepatic organic anion-transporting polypeptides and P-glycoprotein (ABCB1) protein expression: quantification by liquid chromatography tandem mass spectroscopy and influence of genotype, age, and sex

Interindividual variability in protein expression of organic anion-transporting polypeptides (OATPs) OATP1B1, OATP1B3, OATP2B1, and multidrug resistance-linked P-glycoprotein (P-gp) or ABCB1 was quantified in frozen human livers (n = 64) and cryopreserved human hepatocytes (n = 12) by a validated liquid chromatography tandem mass spectroscopy (LC-MS/MS) method. Membrane isolation, sample workup, and LC-MS/MS analyses were as described before by our laboratory. Briefly, total native membrane proteins, isolated from the liver tissue and cryopreserved hepatocytes, were trypsin digested and quantified by LC-MS/MS using signature peptide(s) unique to each transporter. The mean ± S.D. (maximum/minimum range in parentheses) protein expression (fmol/µg of membrane protein) in human liver tissue was OATP1B1- 2.0 ± 0.9 (7), OATP1B3- 1.1 ± 0.5 (8), OATP2B1- 1 1.7 ± 0.6 (5), and P-gp- 0.4 ± 0.2 (8). Transporter expression in the liver tissue was comparable to that in the cryopreserved hepatocytes. Most important is that livers with SLCO1B1 (encoding OATP1B1) haplotypes *14/*14 and *14/*1a [i.e., representing single nucleotide polymorphisms (SNPs), c.388A > G, and c.463C > A] had significantly higher (P < 0.0001) protein expression than the reference haplotype (*1a/*1a). Based on these genotype-dependent protein expression data, we predicted (using Simcyp) an up to ∼40% decrease in the mean area under the curve of rosuvastatin or repaglinide in subjects harboring these variant alleles compared with those harboring the reference alleles. SLCO1B3 (encoding OATP1B3) SNPs did not significantly affect protein expression. Age and sex were not associated with transporter protein expression. These data will facilitate the prediction of population-based human transporter-mediated drug disposition, drug-drug interactions, and interindividual variability through physiologically based pharmacokinetic modeling.

Moving from basic toward systems pharmacodynamic models

Building upon many classical foundations of pharmacology, a diverse array of mechanistic pharmacokinetic-pharmacodynamic (PK/PD) models have emerged based on mechanisms of drug action and primary rate-limiting or turnover processes in physiology. An array of basic models can be extended to handle various complexities including tolerance and can readily be employed as building blocks in assembling enhanced PK/PD or small systems models. Our corticosteroid models demonstrate these concepts as well as elements of horizontal and vertical integration of molecular to whole-body processes. The potential advantages and challenges in moving PK/PD toward systems models are described.