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Recombinant human erythropoietin (rHuEPO) is effective in managing chronic kidney disease and chemotherapy-induced anemia. However, hyporesponsiveness to rHuEPO treatment was reported in about 10% of the patients. A decreased response in rats receiving a single or multiple doses of rHuEPO was also observed. In this study, we aimed to develop a quantitative systems pharmacology (QSP) model to examine hyporesponsiveness to rHuEPO in rats. Pharmacokinetic (PK) and pharmacodynamic (PD) data after a single intravenous dose of rHuEPO (100 IU/kg) was obtained from a previous study (Yan et al. in Pharm Res, 30:1026-1036, 2013) including rHuEPO plasma concentrations, erythroid precursors counts in femur bone marrow and spleen, reticulocytes (RETs), red blood cells (RBCs), and hemoglobin (HGB) in circulation. Parameter values were obtained from literature or calibrated with experimental data. Global sensitivity analysis and model-based simulations were performed to assess parameter sensitivity and hyporesponsiveness. The final QSP model adequately characterizes time courses of rHuEPO PK and nine PD endpoints in both control and treatment groups simultaneously. The model indicates that negative feedback regulation, neocytolysis, and depletion of erythroid precursors are major factors leading to hyporesponsiveness to rHuEPO treatment in rats.

Population pharmacokinetic/pharmacodynamic (PK/PD) analysis was performed for extensive data for differing dosage forms and routes for dexamethasone (DEX) and betamethasone (BET) in 48 healthy nonpregnant Indian women in a partial and complex cross-over design. Single doses of 6 mg dexamethasone phosphate (DEX-P), betamethasone phosphate (BET-P), or 1:1 mixture of betamethasone phosphate and acetate (BET-PA) were administered orally (PO) or intramuscularly (IM) where each woman enrolled in a two-period cross-over study. Plasma concentrations collected over 96 h were described with a two-compartment model with differing PO and IM first-order absorption inputs. Overall, BET exhibited slower clearance, similar volume of distribution, faster absorption, and longer persistence than DEX with BET acetate producing extremely slow absorption but full bioavailability of BET. Six biomarkers were assessed over a 24-h baseline period with four showing circadian rhythms with complex baselines. These baselines and the strong responses seen after drug dosing were fitted with various indirect response models using the Laplace estimation methods in NONMEM 7.4. Both the PK and six biomarker responses were well-described with modest variability likely due to the homogeneous ages, weights, and ethnicities of the women. The drugs either inhibited or stimulated the influx processes with some models requiring joint inclusion of drug effects on circadian cortisol suppression. The biomarkers and order of sensitivity (lowest IC50/SC50 to highest) were: cortisol, T-helper cells, basophils, glucose, neutrophils, and T-cytotoxic cells. DEX sensitivities were generally greater than BET with corresponding mean ratios for these biomarkers of 2.86, 1.27, 1.72, 1.27, 2.69, and 1.06. Overall, the longer PK (e.g. half-life) of BET, but lesser PD activity (e.g. higher IC50), produces single-dose response profiles that appear quite similar, except for the extended effects from BET-PA. This comprehensive population modeling effort provides the first detailed comparison of the PK profiles and six biomarker responses of five commonly used dosage forms of DEX and BET in healthy women.

Population analysis of pharmacokinetic data for five differing dosage forms and routes for dexamethasone and betamethasone in 48 healthy nonpregnant Indian women was performed that accounted for a partial and complex cross-over design. Single doses of 6 mg dexamethasone phosphate (DEX-P), betamethasone phosphate (BET-P), or 1:1 mixture of betamethasone phosphate and acetate (BET-PA) were administered orally (PO) or intramuscularly (IM). Plasma concentrations collected for two periods over 96 h were described with a two-compartment model with differing PO and IM first-order absorption inputs. Clearances and volumes were divided by the IM bioavailability $${F}_{IM}$$ F IM . The homogeneous ages, body weights, and ethnicity of the women obviated covariate analysis. Parameter estimates were obtained by the Laplace estimation method implemented in NONMEM 7.4. Typical values for dexamethasone were clearance ($${CL/F}_{IM})$$ C L / F IM ) of 9.29 L/h, steady-state volume ($${{V}_{ss}/F}_{IM})$$ V ss / F IM ) of 56.4 L, IM absorption constant $$\left({k}_{aIM}\right)$$ k aIM of 0.460 1/h and oral absorption constant ($${k}_{aPO})$$ k aPO ) of 0.936 1/h. Betamethasone parameters were CL/FIM of 5.95 L/h, $${Vss/F}_{IM}$$ V s s / F IM of 72.4 L, $${k}_{aIM}$$ k aIM of 0.971 1/h, and $${k}_{aPO}$$ k aPO of 1.21 1/h. The PO to IM F values were close to 1.0 for both drugs. The terminal half-lives averaged about 7.5 h for DEX, 17 h for BET, and 78 h for BET from BET-PA with the latter reflecting very slow release of BET from the acetate ester. Overall, BET exhibited slower clearance, larger volume of distribution, faster absorption, and longer persistence than DEX. These data may be useful in considering exposures when substituting one form of corticosteroid for another.

In many models of pharmacodynamic systems with delays, a delay of an input is introduced by means of the convolution with the gamma distribution. An approximation of the convolution integral of bound functions based on a system of ordinary differential equations that utilizes properties of the binomial series has been introduced. The approximation converges uniformly on every compact time interval and an estimate of the approximation error has been found $$O\left( {\frac{1}{{N^{\nu } }}} \right)$$ where $$N$$ is the number of differential equations and $$\nu$$ is the shape parameter of the gamma distribution. The accuracy of approximation has been tested on a set of input functions for which the convolution is known explicitly. For tested functions, $$N \ge 20$$ has resulted in an accurate approximation, if $$\nu \ge 1$$ . However, if $$\nu < 1$$ the error of approximation decreases slowly with increasing $$N$$ , and $$N > 100$$ might be necessary to achieve acceptable accuracy. Finally, the approximation was applied to estimate parameters for the distributed delay model of chemotherapy-induced myelosuppression from previously published WBC count data in rats treated with 5-fluorouracil.

The distributed delay model has been introduced that replaces the transit compartments in the classic model of chemotherapy-induced myelosuppression with a convolution integral. The maturation of granulocyte precursors in the bone marrow is described by the gamma probability density function with the shape parameter (ν). If ν is a positive integer, the distributed delay model coincides with the classic model with ν transit compartments. The purpose of this work was to evaluate performance of the distributed delay model with particular focus on model deterministic identifiability in the presence of the shape parameter. The classic model served as a reference for comparison. Previously published white blood cell (WBC) count data in rats receiving bolus doses of 5-fluorouracil were fitted by both models. The negative two log-likelihood objective function (-2LL) and running times were used as major markers of performance. Local sensitivity analysis was done to evaluate the impact of ν on the pharmacodynamics response WBC. The ν estimate was 1.46 with 16.1% CV% compared to ν = 3 for the classic model. The difference of 6.78 in - 2LL between classic model and the distributed delay model implied that the latter performed significantly better than former according to the log-likelihood ratio test (P = 0.009), although the overall performance was modestly better. The running times were 1 s and 66.2 min, respectively. The long running time of the distributed delay model was attributed to computationally intensive evaluation of the convolution integral. The sensitivity analysis revealed that ν strongly influences the WBC response by controlling cell proliferation and elimination of WBCs from the circulation. In conclusion, the distributed delay model was deterministically identifiable from typical cytotoxic data. Its performance was modestly better than the classic model with significantly longer running time.

Dexmedetomidine (DEX) is a fairly new alfa2-agonist which has been increasingly used in recent years for analgosedation, mostly because it offers a unique ability of providing both moderate level of sedation and analgesia without respiratory depression. Despite of many papers published, there are still only a few concerning the PK of the drug given as long-term infusion in ICU patients. The aim of this work was to characterize the population pharmacokinetics of dexmedetomidine and to investigate the potential benefits of individualization of drug dosing based on patient characteristics in the heterogeneous group of medical and surgical patients staying in intensive care unit. This study was performed in the group of 17 males and 10 females patients with a median age of 59.5 years and median body weight of 75 kg. Blood samples for dexmedetomidine assay were collected from arterial catheter, during and after discontinuation of a standard infusion, that ranged from 24 to 102 h. The following covariates were examined to influence dexmedetomidine PK: age, sex, body weight, patients’ health status described by Sequential Organ Failure Assessment Score (SOFA), inotropes usage, and infusion duration. The dexmedetomidine PK was best described by a two-compartment model. The typical values of PK parameters were estimated as 27 L for the volume of the central compartment, 87.6 L for the volume of the peripheral compartment, 38.5 L/h (9.2 mL/min/kg for a 70 kg patient) for systemic clearance and 46.4 L/h for the distribution clearance. Those values are consistent with literature findings. We were unable to show any significant relationship between collected covariates and dexmedetomidine PK. This study does not provide sufficient evidence to support the individualization of dexmedetomidine dosing based on age, sex, body weight, SOFA, and infusion duration. Electronic supplementary material The online version of this article (10.1007/s10928-017-9564-7) contains supplementary material, which is available to authorized users.

Age-structured cell population model was introduced to describe cell survival. The impact of the environment on the cell population is represented by drug plasma concentration. A key model variable is the hazard of cell removal that is a subject to the environment effect. The model is capable of describing cohort and random labeling cell survival data. In addition, it accounts for cell loss due to labeling of cell sample, but it lacks ability to describe the effect of label elution on the survival data. The model was applied to red blood cell (RBC) survival data in two groups of Wistar rats obtained by two techniques: cohort labeling using 14C-glycine (N = 4) and random labeling using biotin (N = 8). The Weibull probability density function was selected for the RBC lifespan distribution. The data were simultaneously fitted by the mixed effects model implemented in Monolix 4.3.3. The estimated typical values of RBC lifespan and age were 53.7 and 27.8 days, respectively. A noticeable effect of biotinylation on RBC survival was observed that resulted in a significant difference between the means of individual RBC lifespan for two groups. The model provides a mechanistic framework flexible enough to account for various experimental designs to generate the cell survival data. Despite model qualification using animal data, the model has the same potential to be applied to cell survival data analysis in humans.

Rhythmicity in baseline responses over a 24-h period for an indirect pharmacological effect R(t) can arise from either a periodic time-dependent input rate $$ k_{in} \left( t \right) $$ or a periodic time-dependent loss constant $$ k_{out} \left( t \right) $$ . If either $$ k_{in} \left( t \right) $$ or $$ k_{out} \left( t \right) $$ follows some nonstationary biological rhythm (e.g., circadian), then the response R(t) also displays a periodic behavior. Indirect response models assuming time-dependent input rates $$ \left[ {k_{in} \left( t \right)} \right] $$ have been utilized to capture drug effects on various physiological responses such as hormone suppression, immune cell trafficking, and gene expression in tissues. This paradigm was extended to consider responses with circadian-controlled loss $$ \left[ {k_{out} \left( t \right)} \right] $$ mechanisms. Theoretical equations describing this model are presented and simulations were performed to examine expected response behaviors. The model was able to capture the chronobiology and pharmacodynamics of applicable drug responses, including the uricosuric effects of lesinurad in humans, suppression of the beta amyloid (Aβ) peptide by a gamma-secretase inhibitor in mouse brain, and the modulation of extracellular dopamine by a dopamine transporter inhibitor in rat brain. This type of model has a mechanistic basis and shows utility for capturing drug responses displaying nonstationary baselines controlled by removal mechanism(s).

In pharmacokinetics/pharmacodynamics (PKPD) the measured response is often delayed relative to drug administration, individuals in a population have a certain lifespan until they maturate or the change of biomarkers does not immediately affects the primary endpoint. The classical approach in PKPD is to apply transit compartment models (TCM) based on ordinary differential equations to handle such delays. However, an alternative approach to deal with delays are delay differential equations (DDE). DDEs feature additional flexibility and properties, realize more complex dynamics and can complementary be used together with TCMs. We introduce several delay based PKPD models and investigate mathematical properties of general DDE based models, which serve as subunits in order to build larger PKPD models. Finally, we review current PKPD software with respect to the implementation of DDEs for PKPD analysis.

We aimed to develop a cell-level pharmacodynamics-mediated drug disposition (PDMDD) model to analyze in vivo systems where the PD response to a drug has an appreciable effect on the pharmacokinetics (PK). An existing cellular level model of PD stimulation was combined with the standard target-mediated drug disposition (TMDD) model and the resulting model structure was parametrically identifiable from typical in vivo PK and PD data. The PD model of the cell population was controlled by the production rate k in and elimination rate k out which could be stimulated or inhibited by the number of bound receptors on a single cell. Simulations were performed to assess the impact of single and repeated dosing on the total drug clearance. The clinical utility of the cell-level PDMDD model was demonstrated by fitting published data on the stimulatory effects of filgrastim on absolute neutrophil counts in healthy subjects. We postulated repeated dosing as a means of detecting and quantifying PDMDD as a single dose might not be sufficient to elicit the cellular response capable of altering the receptor pool to visibly affect drug disposition. In the absence of any PD effect, the model reduces down to the standard TMDD model. The applications of this model can be readily extended to include chemotherapy-induced cytopenias affecting clearance of endogenous hematopoietic growth factors, different monoclonal antibodies and immunogenicity effects on PK.

The target-mediated drug disposition (TMDD) model has been adopted to describe pharmacokinetics for two drugs competing for the same receptor. A rapid binding assumption introduces total receptor and total drug concentrations while free drug concentrations CA and CB are calculated from the equilibrium (Gaddum) equations. The Gaddum equations are polynomials in CA and CB of second degree that have explicit solutions involving complex numbers. The aim of this study was to develop numerical methods to solve the rapid binding TMDD model for two drugs competing for the same receptor that can be implemented in pharmacokinetic software. Algebra, calculus, and computer simulations were used to develop algorithms and investigate properties of solutions to the TMDD model with two drugs competitively binding to the same receptor. A general rapid binding approximation of the TMDD model for two drugs competing for the same receptor has been proposed. The explicit solutions to the equilibrium equations employ complex numbers, which cannot be easily solved by pharmacokinetic software. Numerical bisection algorithm and differential representation were developed to solve the system instead of obtaining an explicit solution. The numerical solutions were validated by MATLAB 7.2 solver for polynomial roots. The applicability of these algorithms was demonstrated by simulating concentration-time profiles resulting from exogenous and endogenous IgG competing for the neonatal Fc receptor (FcRn), and darbepoetin competing with endogenous erythropoietin for the erythropoietin receptor. These models were implemented in Phoenix WinNonlin 6.0 and ADAPT 5, respectively.

Target-mediated drug disposition (TMDD) models have been applied to describe the pharmacokinetics of drugs whose distribution and/or clearance are affected by its target due to high binding affinity and limited capacity. The Michaelis-Menten (M-M) model has also been frequently used to describe the pharmacokinetics of such drugs. The purpose of this study is to investigate conditions for equivalence between M-M and TMDD pharmacokinetic models and provide guidelines for selection between these two approaches. Theoretical derivations were used to determine conditions under which M-M and TMDD pharmacokinetic models are equivalent. Computer simulations and model fitting were conducted to demonstrate these conditions. Typical M-M and TMDD profiles were simulated based on literature data for an anti-CD4 monoclonal antibody (TRX1) and phenytoin administered intravenously. Both models were fitted to data and goodness of fit criteria were evaluated for model selection. A case study of recombinant human erythropoietin was conducted to qualify results. A rapid binding TMDD model is equivalent to the M-M model if total target density R ( tot ) is constant, and R ( tot ) K ( D ) /(K ( D ) + C) ( 2 )1 where K ( D ) represents the dissociation constant and C is the free drug concentration. Under these conditions, M-M parameters are defined as: V ( max ) = k ( int ) R ( tot ) V ( c ) and K ( m ) = K ( D ) where k ( int ) represents an internalization rate constant, and V ( c ) is the volume of the central compartment. R ( tot ) is constant if and only if k ( int ) = k ( deg,) where k ( deg ) is a degradation rate constant. If the TMDD model predictions are not sensitive to k ( int ) or k ( deg ) parameters, the condition of R ( tot ) K ( D ) /(K ( D ) + C) ( 2 )1 alone can preserve the equivalence between rapid binding TMDD and M-M models. The model selection process for drugs that exhibit TMDD should involve a full mechanistic model as well as reduced models. The best model should adequately describe the data and have a minimal set of parameters estimated with acceptable precision.

This report generates efficient experimental designs (dose, sampling times) for parameter estimation for four basic physiologic indirect pharmacodynamic response (IDR) models. The principles underlying IDR models and their response patterns have been well described. Each IDR model explicitly contains four parameters, k (in) (production), k (out) (loss), I (max)/S (max) (capacity) and IC (50)/SC (50) (sensitivity). The pharmacokinetics of an IV dose of drug described by a monoexponential function of time with two parameters, V and k (el), is assumed. The random errors in the response variable are assumed to be additive, independent, and normal with zero mean and variance proportional to some power of the mean response. Optimal design theory was used extensively to assess the role of both dose and sampling times. Our designs were generated in Mathematica (ADAPT 5 typically produces identical results). G-optimality was used to verify that the generated designs were indeed D-optimal. Such designs are efficient and robust when good prior knowledge of the estimated parameters is available. The efficiency of unconstrained D-optimal designs (4 dose, sampling time pairs) does not improve much when the drug doses are allowed to differ, compared with constrained single dose designs (4 sampling times) with one maximal feasible dose. Also, explored were efficiencies of alternative study designs and results from parameter misspecification. This analysis substantiates the importance of larger doses yielding greater certainty in parameter estimation in pharmacodynamics.

A mechanism-based pharmacokinetic–pharmacodynamic (PK/PD) model was developed for recombinant human erythropoietin (rHuEPO) to account for receptor-mediated endocytosis via erythropoietin receptor (EPOR) as a primary mechanism for nonlinear disposition of rHuEPO as well as activation of erythropoietic stimulation. Time profiles of rHuEPO concentrations following a wide range of intravenous (i.v.) doses in rats (10, 100, 450, 1,350, 4,050 IU/kg), monkeys (500, 2,000, 4,000 IU/kg), and man (10, 100, 150, 300, 500 IU/kg) were examined. The mean data of reticulocytes, red blood cells (RBC), and hemoglobin for five different doses in rats were analyzed. The PK model components included receptor binding, subsequent internalization and degradation, EPOR turnover, non-specific tissue distribution, and linear first-order elimination from plasma. The equilibrium dissociation constant (K D ) was similar between rats and monkeys (0.11 nM) and was 10-fold lower in humans (0.012 nM). The PD effects of rHuEPO were described by an indirect response model with lifespan cell loss and driven by the rHuEPO–EPOR complex. A generalized nonlinear PK model for rHuEPO taking into account EPOR binding of the drug in bone marrow was proposed and well described the PK profiles of rHuEPO following i.v. doses in rats, monkeys, and man. The present receptor-mediated PK/PD model for rHuEPO closely reflects underlying mechanisms of disposition and dynamics of rHuEPO.

The purpose of this work is to review basic pharmacodynamic (PD) models describing drug effects on cell populations and expand them to age-structured models using the theory of physiologically structured populations. The plasma drug concentrations are interpreted as the environment affecting the cell production and mortality rates. An explicit solution to model equations provides the age density distribution that serves to establish a relationship between the cell lifespan distribution and the hazard of cell removal. Given the lifespan distributions, the age distributions for most commonly applied PD models of cell responses including basic cell turnover, transit compartments, and basic lifespan models have been derived both for the baseline conditions and drug treatment. The steady-state age distribution for basic indirect response models is exponential, and it is uniform for the basic lifespan model. As an example of more complex cell population, the age distribution of human red blood cells has been simulated based on a recent model of red blood cell survival. The age distribution for cells in the transit compartment model is the sum of the gamma functions. Means and variances of age distributions for all discussed models were calculated. A brief discussion of numerical challenges and possible future model developments is presented.

A mechanistic model describing the effects of chemotherapy and radiation on platelet counts and endogenous thrombopoietin (eTPO) in mice was developed. Thrombocytopenia was induced in mice by injection of carboplatin followed by the whole body irradiation on days 0, 28, and 56, with platelet and eTPO samples collected over 84 days. The pharmacodynamic model consisted of a series of aging compartments representing proliferating megakaryocyte precursors, megakaryocytes, and platelets with possible eTPO clearance through internalization. The cytotoxic effects of treatment were described by the kinetics of the effect (K-PD) model, and stimulation of platelet production by eTPO was considered to be driven by receptor occupancy. The proposed PD model adequately described the platelet counts and eTPO concentrations in mice by accounting for nadirs and peaks of platelet count, and rebounds in eTPO time course profiles. The estimates of model parameters were in good agreement with their physiological values reported in literature for mice with platelet lifespan of 4.3 days and 185 cMpl receptors per platelet. The predicted duration of the treatment effect was 0.82 h (approximately 5 carboplatin half-lives in mice). The data was not informative about the eTPO stimulatory effect as the nominal precursor production rate was sufficient to account for platelet response to treatment. The model quantified the inverse relationship between eTPO levels and platelet counts and offered an explanation of the tolerance effect observed in the eTPO data. The simulated rebound in free receptors levels correlated with rebounds in eTPO levels. The model suggests that the duration of the toxic effects is determined by the turnover of the proliferating cells in the bone marrow. This indicates that the lifespan of the target cells (megakaryocyte precursors, megakaryocytes and platelets) is a key determinant in the duration of both drug exposure and toxicity due to treatment. The model can be extended to account for pharmacokinetics of exogenous drugs and be applied to analysis of human data.

Precursor-dependent indirect response (PDIDR) models may describe the tolerance and rebound phenomenon observed for many pharmacodynamic responses where these characteristics are manifested due to the depletion or accumulation of a physiological precursor or cofactor pool responsible for generating drug effects. The purpose of this report is to extend the concepts and applications of these models and to approximate responses and limiting conditions for very large doses of drugs. Asymptotic analysis was performed for qualitative determination of various parameters, such as maximum response (Rmax) and rebound (RBmax), time to maximum response and rebound (TRmax and TRBmax), and area under the effect and rebound curve (ABEC and ABRC) for large doses. Computer simulations were performed to assess the role of dose for both cases where drugs act either by depleting (Model V) or by blocking (Model VI) the endogenous precursor. Simulations showed that Rmax, RBmax, TRBmax, ABEC and ABRC increase with dose, eventually reaching a plateau when Dose/V is very large compared to the efficacy parameters (SC50 or IC50) of the drug. However, TRmax either increased or decreased with dose depending on various system and drug parameters. The limits for these parameters at large doses qualitatively determined by asymptotic analysis closely approximated the plateaus observed from the simulated curves. At large doses, the drug response could be approximated by a Bateman-like function for both Models V and VI. Qualitative analyses along with simulation studies provide a fundamental basis for understanding the temporal aspects of the PDIDR models especially at large doses to describe the tolerance and rebound phenomenon.

The application of D-optimization and the assessment of bias and precision of parameter estimates for four basic pharmacodynamic (PD) indirect response (IDR) models for ascending doses was examined using simulated data. While D-optimization provided four sampling times, each IDR model was used to generate eight data points per dose level. The PD parameters were: input rate constant (k in), disposition rate constant (k out), capacity constant (I max or S max), and sensitivity constant (IC 50 or SC 50). A monoexponential pharmacokinetic function was applied with single doses increased by a factor of 10 to generate responses that vary from weak to fully saturable. For each dose, 100 replications of response data were simulated using independent normally distributed errors of CV = 20%. The original IDR model was fitted and PD parameters estimated. Histograms and descriptive statistics were generated. All parameters exhibited asymmetric distributions with positive coefficients of skewness except for I max. Higher doses resulted in unbiased estimates of all PD parameters. The precision of parameter estimates improved with increasing doses except for IC 50 and SC 50 indicating that a single dose experimental design cannot be corrected by increasing dose in order to improve precision of estimates of IC 50 or SC 50. Highest variability was for IC 50 and SC 50 parameters. This study provides new insights into optimum study designs and recovery of parameters for basic IDR models.

Indirect pharmacodynamic response (IDR) models were developed for agents which alter the generation of cell populations with arbitrary lifespan distributions. These models extend lifespan based IDR models introduced previously [J. Pharmacokinet. Biopharm. 27: 467, 1999] for cell populations with the same lifespan (“delta” distribution). Considered are cell populations exhibiting time-invariant lifespan distributions described by the probability density function l(τ). It is assumed that cell response (R) is produced at a zero-order rate (k in(t)) and is eliminated from the population when the cell lifespan expires. The cell loss rate is calculated as k in*l(t), where ‘*’ denotes the convolution operator. Therapeutic agents can stimulate or inhibit production rates according to the Hill function: 1 ± H(C(t)) where H(C(t)) contains the capacity (S max) and potency (SC 50) parameters and C(t) is a pharmacokinetic function. The production rate is k in(t)=k in· [ 1±H(C(t))]. The operational model is dR/dt = k in(t)−k in*l(t) with the baseline condition R 0 = k in· T R , where T R is the mean lifespan. Single populations as well as populations with precursors were examined by simulation to establish the role of lifespan distribution parameters (mean and standard deviation) in controlling the response vs. time profile. Estimability of parameters was assessed. Numerical techniques of solving differential equations with the convolution integral were proposed. In addition, the models were applied to literature data to describe the stimulatory effects of single doses of recombinant human erythropoietin on reticulocytes in blood. The estimates of S max and SC 50 for these agents were obtained along with means and standard deviations for reticulocyte lifespan distributions. The proposed models can be used to analyze the pharmacodynamics of agents which alter natural cell production yielding parameters describing their efficacy and potency as well as means and standard deviations for cell lifespan distributions.

Many physiological factors are regulated by homeostatic mechanisms to maintain normal body function. Empirical lower R l (Model I and IV) or upper Rh limits (Model II and III) were included in current basic indirect response (IDR) models to account for the additional role of physiological limits (IDRPL). Various characteristics of these models were evaluated with simulations and explicit equations. The simulations reveal that the expanded models exhibit most properties of basic indirect response models, such as slow response initiation, lag time between the kinetic and dynamic peaks, a large dose plateau, and shift in Tmax with dose. The proposed models always produce lesser net responses than predicted by basic IDR models. Simulations demonstrate that addition of a parameter limit which is close to the baseline has a great influence on the overall and maximum responses and fitted model parameters. Only stimulatory IDRPL Models III and IV allow resolution of all model parameters in the absence of clear indications or predetermined values of the lower or upper limits. However, all four models are able to resolve model parameters when subgroups with different baselines are simultaneously fitted. These models create new interpretations of the determinants of baseline conditions which can be important in assessing inter-subject variability in responses. The applicability of IDRPL models is demonstrated using several examples from the published literature. Indirect response models with physiological limits will be useful in characterizing drug responses for turnover systems which are maintained within a certain range.

Pharmacokinetic/pharmacodynamic (PK/PD) models for hematological drug effects exist that assume that cells are produced by a zero- or first-order process, survive for a specific duration (cell lifespan), and then are lost. Due to the fact that delay differential equations (DDE) are needed for cell lifespan models, their software implementation is not straightforward. Our objective is to demonstrate methods to implement three different cell lifespan models for dealing with hematological drug effects and to evaluate the performance of NONMEM to estimate the model parameters. For the basic lifespan indirect response (LIDR) model, cells are produced by a zero-order process and removed due to senescence. The modified LIDR model adds a precursor pool. The LIDR model of cytotoxicity assumes a three-pool indirect model to account for the cell proliferation with capacity-limited cytotoxicity followed by maturation, and removal from the circulation. A numerical method (method of steps) implementing DDE in NONMEM was introduced. Simulation followed by estimation was used to evaluate NONMEM performance and the impact of the minimization algorithm (first-order method vs. first-order conditional estimation method) and the model for residual variability on the estimates of the population parameters. The FOCE method combined with log-transformation of data was found to be superior. This report provides methodology that will assist in application of population methods for assessing hematological responses to various types of drugs.

The assessment of drug interactions for a simple turnover system when the basic pharmacodynamic response is governed by indirect mechanisms was explored. This report describes a diverse array of possible in vivo pharmacodynamic effects from a combination of two drugs acting by similar or different indirect mechanisms. Various conditions of pharmacodynamic drug combinations were explored mathematically and by simulation: (a) interactions of two drugs acting simultaneously either on the production (k(in)) or on the dissipation (k(out)) processes controlling the in vivo response by competitive (four cases) or non-competitive interaction (six cases); and (b) combinations of two drugs acting on separate k(in) and k(out) processes simultaneously (four cases). A range of different combinations of drug doses was used. Plasma concentration time profiles were generated according to monoexponential disposition. Pharmacodynamic response profiles were simulated using the above conditions and characterized by descriptors such as Area Between Effect (and Baseline) Curve (ABEC) values. The interaction of agents by competitive mechanisms produced net responses that were additive in nature. Response profiles for non-competitive interactions on the same process were both antagonistic (for two drugs with effects that oppose each other) and synergistic (for two drugs that produce the same response). On the other hand, response signatures for agents acting non-competitively on the production and dissipation factors by opposing mechanisms (e.g. inhibiting k(in) plus stimulating k(out)) showed dramatic changes in net effects and produced apparent drug synergy. Net indirect response profiles for joint use of two or more drugs measured by ABEC values may look "additive", "antagonistic", or "synergistic" depending on doses, their intrinsic potencies, the nature of interaction (competitive or non-competitive) as well as their mechanisms of action. These models may help explain changes in pharmacologic responses to two agents in a more rational and mechanistic fashion than older empirical methods.

The leukopenic effects of anticancer agents are described using a semi-physiologic multiple-pool cell lifespan model. The time course of myelosuppression in relation to the drug concentration vs. time profile was characterized using a three pool indirect model. The proliferation and maturation stages of myeloid cells in the bone marrow and cell removal from the circulation were quantitated with a cell life-span concept. Drug effects were assumed to take place in the bone marrow based on irreversible linear or capacity-limited cytotoxicity. Mathematical derivations and computer simulations (Adapt II) were used to examine the properties of the model. Data from the literature were also analyzed. Cell response profiles after therapy typically exhibit a lag period, reduction to a nadir, and return to baseline. The predicted values of the time periods of granulopoiesis were 10-14 days for proliferation, and 1-6 days for maturation of progenitor cells in the bone marrow. The proposed irreversible mechanism of cell killing by anticancer drugs explains previously observed relationships between leukocyte nadir counts and exposure to the drug and/or duration of drug concentrations above some threshold level. The model was applied to literature data for paclitaxel and etoposide effects on leukocyte counts. The predicted value of KC50 for paclitaxel ranged from 0.004 to 0.2 microgram/mL and for etoposide 2 micrograms/mL. The present model accounts for drug-induced leukopenia using a physiologic cell production and loss model and irreversible cytotoxicity in a precursor pool.

Basic indirect response models where drug alters the production (kin) of the response variable (R) based on the Hill function previously assumed one-compartment distribution of the response variable and simple first-order loss (kout) of R. These models were extended using convolution theory to consideration of two-compartment distribution of R and/or polyexponential loss of R. Theoretical equations and methods of data analysis were developed and simulations are provided to demonstrate expected response behavior based on biexponential response dissipation. The inhibition model was applied to our previous data for inhibition of circadian cortisol secretion by prednisolone. The presence of multicompartment response variables and/or polyexponential loss complicates the response patterns and resolution of pharmacologic parameters of indirect response models and requires careful experimental and data analysis approaches in order to properly evaluate such pharmacodynamic responses. The occurrence of these alternative distribution or disposition components does not alter the area under the effect curve (AUCE) which remains identical to the basic models. Model misselection was addressed by testing fittings comparing the basic and new models. Use of the former for these more complex models does not severely perturb the calculated cardinal dynamic parameters. These models may provide improved insights into indirect responses with complexities in distribution or disposition.

Basic indirect pharmacodynamic models for agents which alter the generation of natural cells based on a life-span concept are introduced. It is assumed that cells (R) are produced at a constant rate (kin), survive for a specific duration TR, and then are lost. The rate of cell loss must equal the production rate but is delayed by TR, A therapeutic agent can stimulate or inhibit the production rate according to the Hill function: 1 ±H(C(t) ) where H(C(t)) contains capacity (Smax) and sensitivity (SC50) constants and C(t) is a pharmacokinetic function. Thus an operative model is dR/dt = kin • [1 ± H(C (t))J-ktn • [1 ± H(C(t-T R))]

The Michaelis-Menten (M-M) approximation of the target-mediated drug disposition (TMDD) pharmacokinetic (PK) model was derived based on the rapid binding (RB) or quasi steady-state (QSS) assumptions that implied that the target and drug binding and dissociation were in equilibrium. However, the initial dose for an IV bolus injection for the M-M model did not account for a fraction bound to the target. We postulated a correction to an initial condition that was consistent with the assumptions underlying the M-M approximation. We determined that the difference between the injected dose and one that should be used for the initial condition is equal to the amount of drug bound to the target upon reaching the equilibrium. We also observed that the corrected initial condition made the internalization rate constant an identifiable parameter that was not for the original M-M model. Finally, we performed a simulation exercise to check if the correction will impact the model performance and the bias of the M-M parameter estimates. We used literature data to simulate plasma drug concentrations described by the RB/QSS TMDD model. The simulated data were refitted by both models. All the parameters estimated from the original M-M model were substantially biased. On the other hand, the corrected M-M is able to accurately estimate these parameters except for equilibrium constant K(m). Weighted sum of square residual and Akaike information criterion suggested a better performance of the corrected M-M model compared with the original M-M model. Further studies are necessary to determine the importance of this correction for the M-M model applications to analysis of TMDD driven PK data.

In the field of hematology, several mechanism-based pharmacokinetic-pharmacodynamic models have been developed to understand the dynamics of several blood cell populations under different clinical conditions while accounting for the essential underlying principles of pharmacology, physiology and pathology. In general, a population of blood cells is basically controlled by two processes: the cell production and cell loss. The assumption that each cell exits the population when its lifespan expires implies that the cell loss rate is equal to the cell production rate delayed by the lifespan and justifies the use of delayed differential equations for compartmental modeling. This review is focused on lifespan models based on delayed differential equations and presents the structure and properties of the basic lifespan indirect response (LIDR) models for drugs affecting cell production or cell lifespan distribution. The LIDR models for drugs affecting the precursor cell production or decreasing the precursor cell population are also presented and their properties are discussed. The interpretation of transit compartment models as LIDR models is reviewed as the basis for introducing a new LIDR for drugs affecting the cell lifespan distribution. Finally, the applications and limitations of the LIDR models are discussed.

Target mediated drug disposition (TMDD) describes the phenomenon where high affinity binding of a drug to its pharmacological target (enzymes or receptors) significantly alters the pharmacokinetic profile of the drug. A rapid binding model replaces the often inestimable binding micro-constants (k (on) and k (off)) of TMDD models with the equilibrium dissociation constant (K (D)) by assuming rapid binding of the drug to its target. The purpose of this study is to examine the validity of the rapid binding assumption and the pharmacokinetic properties of this model. Temporal profiles of free drug in plasma and a non-specific distribution site, free receptor, and the pharmacodynamically relevant, drug-receptor complex obtained from the rapid binding model compared favorably with the full TMDD model for small values of the parameter epsilon, which represents the ratio of the time required for drug-receptor binding relative to the time required for drug to be cleared from the system. The effect of escalating drug doses on the temporal characteristics and the comparison between the two models has been numerically investigated. A closer match between the full and rapid binding models is observed for high doses. Analysis for very large doses (Dose/V (c)) relative to endogenous steady-state receptor concentration (R (ss)), reveals that the rapid binding model reduces to a standard two compartmental model with a plasma compartment with linear drug elimination and a peripheral compartment. Decreasing clearance with increasing dose and decreasing R (ss) indicates that for drugs exhibiting TMDD, the relative ratio of R (ss) and dose is an important determinant of the pharmacokinetic properties rather than the individual parameters alone. An analytical solution derived for clearance shows that the primary elements of the apparent clearance of the drug are the linear clearance given by k (el) V (c), the non-linear clearance due to drug-receptor complex internalization (k (int)), and the ratio of AUC values of the receptor complex to that of free drug. Overall, simulations and analytical techniques applied here provide a better understanding of the validity of the rapid binding model and provide guidelines for its application.

A new class of basic indirect pharmacodynamic models for agents that alter the loss of natural cells based on a lifespan concept are presented. The lifespan indirect response (LIDR) models assume that cells (R) are produced at a constant rate (k(in)), survive during a certain duration T(R), and finally are lost. The rate of cell loss is equal to the production rate but is delayed by T(R). A therapeutic agent can increase or decrease the baseline cell lifespan to a new cell lifespan, T(D), by temporally changing the proportion of cells belonging to the two modes of the lifespan distribution. Therefore, the change of lifespan at time t is described according to the Hill function, H(C(t)), with capacity (E(max)) and sensitivity (EC(50)), and the pharmacokinetic function C(t). A one-compartment cell model was examined through simulations to describe the role of pharmacokinetics, pharmacodynamics and cell properties for the cases where the drug increases (T(D)T(R)) or decreases (T(D)T(R)) the cell lifespan. The area under the effect curve (AUCE) and explicit solutions of LIDR models for large doses were derived. The applicability of the model was further illustrated using the effects of recombinant human erythropoietin (rHuEPO) on reticulocytes. The cases of both stimulation of the proliferation of bone marrow progenitor cells and the increase of reticulocyte lifespans were used to describe mean data from healthy subjects who received single subcutaneous doses of rHuEPO ranging from 20 to 160 kIU. rHuEPO is about 4.5-fold less potent in increasing reticulocyte survival than in stimulating the precursor production. A maximum increase of 4.1 days in the mean reticulocyte lifespan was estimated and the effect duration on the lifespan distribution was dose dependent. LIDR models share similar properties with basic indirect response models describing drug stimulation or inhibition of the response loss rate with the exception of the presence of a lag time and a dose independent peak time. The current concept can be applied to describe the pharmacodynamic effects of agents affecting survival of hematopoietic cell populations yielding realistic physiological parameters.