Comparison of four basic models of indirect pharmacodynamic responses

Semimechanistic models to relate noxious stimulation, movement, and pupillary dilation responses in the presence of opioids

Intraoperative targeting of the analgesic effect still lacks an optimal solution. Opioids are currently the main drug used to achieve antinociception, and although underdosing can lead to an increased stress response, overdose can also lead to undesirable adverse effects. To better understand how to achieve the optimal analgesic effect of opioids, we studied the influence of remifentanil on the pupillary reflex dilation (PRD) and its relationship with the reflex movement response to a standardized noxious stimulus. The main objective was to generate population pharmacodynamic models relating remifentanil predicted concentrations to movement and to pupillary dilation during general anesthesia. A total of 78 patients undergoing gynecological surgery under general anesthesia were recruited for the study. PRD and movement response to a tetanic stimulus were measured multiple times before and after surgery. We used nonlinear mixed effects modeling to generate a population pharmacodynamic model to describe both the time profiles of PRD and movement responses to noxious stimulation. Our model demonstrated that movement and PRD are equally depressed by remifentanil. Using the developed model, we changed the intensity of stimulation and simulated remifentanil predicted concentrations maximizing the probability of absence of movement response. An estimated effect site concentration of 2 ng/ml of remifentanil was found to inhibit movement to a tetanic stimulation with a probability of 81%.

Pharmacokinetic/Pharmacodynamic Modeling of a Cell-Penetrating Peptide Phosphorodiamidate Morpholino Oligomer in mdx Mice

PURPOSE

Peptide-conjugated phosphorodiamidate morpholino oligomers (PPMOs) have shown promise in treating Duchenne muscular dystrophy (DMD). We evaluated a semi-mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) model to capture the relationship between plasma and muscle tissue exposure/response in mdx mice treated by mouse surrogate PPMO.

METHODS

A single or repeated (every 4 weeks for 20 weeks) intravenous PPMO dose was administered to mdx mice (n = 6/timepoint). A PK/PD model was built to characterize data via sequential modeling. A 2-compartment model was used to describe plasma PK. A simultaneous tissue PK/PD model was subsequently developed: 2-compartment model to describe muscle PK; linked to an indirect response model describing stimulation of synthesis of skipped transcript, which was in turn linked to stimulation of synthesis of dystrophin protein expression.

RESULTS

Model performance assessment via goodness-of-fit plots, visual predictive checks, and accurate parameter estimation indicated robust fits of plasma PK and muscle PK/PD data. The model estimated a PPMO tissue half-life of 5 days-a useful parameter in determining the longevity of PPMOs in tissue and their limited accumulation after multiple doses. Additionally, the model successfully described dystrophin expression after single dosing and associated protein accumulation after multiple dosing (increasing ~ twofold accumulation from the first to last dose).

CONCLUSIONS

This first PK/PD model of a PPMO in a DMD disease model will help characterize and predict the time course of PK/PD biomarkers in mdx mice. Furthermore, the model framework can be used to develop clinical PK/PD models and can be extended to other exon-skipping therapies and species.

Population pharmacokinetics and exposure-response modeling for evinacumab in homozygous familial hypercholesterolemia

Evinacumab, an angiopoietin‐like protein 3 (ANGPTL3) inhibitor, has been shown to significantly reduce low‐density lipoprotein cholesterol (LDL‐C) in patients with homozygous familial hypercholesterolemia (HoFH). This work characterized the population pharmacokinetics (PK)/pharmacodynamics (PD) of evinacumab using pooled phase III clinical data. Total evinacumab PK were described by a two‐compartment model with combined linear and saturable (Michaelis–Menten) elimination, and first‐order absorption. At clinically relevant concentrations, plasma drug concentrations were mainly influenced by the linear clearance pathway. Although the maximum target‐mediated rate of elimination (V_max) parameter for the saturable pathway was found to be positively related to baseline ANGPLTL3, variability in body weight contributed more to the variability in evinacumab exposure than variability in ANGPTL3. An effect of HoFH versus healthy volunteers on V_max was also identified. Weight‐based dosing regimens resulted in consistent evinacumab exposure across weight ranges. An indirect exposure–response model adequately described the relationship between evinacumab and LDL‐C, where drug concentration is assumed to inhibit LDL‐C production. The final population PK/PD model included two nonclinically significant covariates (race and baseline body weight) on the maximum drug‐induced inhibitory effect (I_max) and one (baseline LDL‐C) on the evinacumab concentration inducing 50% of I_max (IC₅₀). A smaller IC₅₀ was observed in patients with higher baseline LDL‐C, suggesting greater sensitivity to treatment. Population exposure–response analysis permitted estimation of derived PD parameters and individual LDL‐C levels over time for patients with HoFH. The model accurately predicted the proportion of patients with HoFH achieving prespecified LDL‐C goals with evinacumab during the ELIPSE HoFH study, further supporting a dosing strategy.

Population Modelling of Dexmedetomidine Pharmacokinetics and Haemodynamic Effects After Intravenous and Subcutaneous Administration

Background and Objective

Dexmedetomidine is a potent agonist of α₂-adrenoceptors causing dose-dependent sedation in humans. Intravenous dexmedetomidine is commonly used perioperatively, but an extravascular route of administration would be favoured in palliative care. Subcutaneous infusions provide desired therapeutic plasma concentrations with fewer unwanted effects as compared with intravenous dosing. We aimed to develop semi-mechanistic population models for predicting pharmacokinetic and pharmacodynamic profiles of dexmedetomidine after intravenous and subcutaneous dosing.

Methods

Non-linear mixed-effects modelling was performed using previously collected concentration and haemodynamic effects data from ten (eight in the intravenous phase) healthy human subjects, aged 19–27 years, receiving 1 µg/kg of intravenous or subcutaneous dexmedetomidine during a 10-min infusion.

Results

The absorption of dexmedetomidine from the subcutaneous injection site, and distribution to local subcutaneous fat tissue was modelled using a semi-physiological approach consisting of a depot and fat compartment, while a two-compartment mammillary model explained further disposition. Dexmedetomidine-induced reductions in plasma norepinephrine concentrations were accurately described by an indirect response model. For blood pressure models, the net effect was specified as hyper- and hypotensive effects of dexmedetomidine due to vasoconstriction on peripheral arteries and sympatholysis mediated via the central nervous system, respectively. A heart rate model combined the dexmedetomidine-induced sympatholytic effect, and input from the central nervous system, predicted from arterial blood pressure levels. Internal evaluation confirmed the predictive performance of the final models, as well as the accuracy of the parameter estimates with narrow confidence intervals.

Conclusions

Our final model precisely describes dexmedetomidine pharmacokinetics and accurately predicts dexmedetomidine-induced sympatholysis and other pharmacodynamic effects. After subcutaneous dosing, dexmedetomidine is taken up into subcutaneous fat tissue, but our simulations indicate that accumulation of dexmedetomidine in this compartment is insignificant.

ClinicalTrials.org

NCT02724098 and EudraCT 2015-004698-34

Electronic supplementary material

The online version of this article (10.1007/s40262-020-00900-3) contains supplementary material, which is available to authorized users.

Analyze impact of tumor-associated kinetics on antibody delivery in solid tumors with a physiologically based pharmacokinetics/pharmacodynamics model

Monoclonal antibody (mAb)-based drugs are critical anti-cancer therapies. Unfortunately, therapeutic efficacy can be compromised by spatially heterogeneous intratumoral Ab deposition. Binding-site barriers arising from Ab and tumor-associated kinetics often underlie this phenomenon. Quantitative insight into these issues may lead to more efficient drug delivery. Difficulties in addressing this issue include (1) lack of techniques to quantify critical kinetic events, (2) lack of a pharmacokinetic/pharmacodynamic (PK/PD) model to assess important parameters for specific tumor types, and (3) uncertainty or variability of critical kinetic factors even within a single tumor type. This study developed a mechanism-based PK/PD model to profile heterogeneous distribution of Ab within tumors and tested this model using real-life experimental data. Model simulations incorporating several uncertainties were used to determine how mAb and tumor-associated kinetics influence receptor occupancy. Simulations were also used to predict the potential impact of these findings in preclinical tumor models and human tumors. We found significant differences in tumor-associated kinetics between groups in which mAb therapy was effective versus groups in which it was ineffective. These kinetic differences included rates of tumor-associated antigen (TAA) degradation, TAA expression, apparent flow rates of interstitial fluid, and ratios of Ab-TAA complex internalization to TAA degradation. We found less significant differences in mAb kinetics, including rates of clearance or affinity for target antigens. In conclusion, our mechanism-based PK/PD model suggests that TAA-associated kinetic factors participate more significantly than those associated with the Ab in generating barriers to mAb delivery and distribution in tumors.

Network-Based Systems Analysis Explains Sequence-Dependent Synergism of Bortezomib and Vorinostat in Multiple Myeloma

Bortezomib and vorinostat exhibit synergistic effects in multiple myeloma (MM) cells when given in sequence, and the purpose of this study was to evaluate the molecular determinants of the interaction using a systems pharmacology approach. A Boolean network model consisting of 79 proteins and 225 connections was developed using literature information characterizing mechanisms of drug action and intracellular signal transduction. Network visualization and structural analysis were conducted, and model simulations were compared with experimental data. Critical biomarkers, such as pNFκB, p53, cellular stress, and p21, were identified using measures of network centrality and model reduction. U266 cells were then exposed to bortezomib (3 nM) and vorinostat (2 μM) as single agents or in simultaneous and sequential (bortezomib for first 24 h, followed by addition of vorinostat for another 24 h) combinations. Temporal changes for nine of the critical proteins in the reduced Boolean model were measured over 48 h, and cellular proliferation was measured over 96 h. A mechanism-based systems model was developed that captured the biological basis of a bortezomib and vorinostat sequence-dependent pharmacodynamic interaction. The model was further extended in vivo by linking in vitro parameter values and dynamics of p21, caspase-3, and pAKT biomarkers to tumor growth in xenograft mice reported in the literature. Network-based methodologies and pharmacodynamic principles were integrated successfully to evaluate bortezomib and vorinostat interactions in a mechanistic and quantitative manner. The model can be potentially applied to evaluate their combination regimens and explore in vivo dosing regimens.

Co-formulation of ketoprofen with tulathromycin alters pharmacokinetic and pharmacodynamic profile of ketoprofen in cattle

The current studies aimed to evaluate the pharmacokinetic (PK) and pharmacodynamic (PD) profile and to establish a PK-PD model for ketoprofen in a new fixed combination product containing tulathromycin (2.5 mg/kg) and ketoprofen (3 mg/kg) to treat bovine respiratory disease associated with pyrexia in cattle. Firstly, the effect of different ketoprofen doses as mono-substance (1, 3, and 6 mg/kg subcutaneous) on lipopolysaccharide-induced fever was evaluated which indicated that rectal temperature reduction lasted longer in the calves receiving 3 and 6 mg/kg ketoprofen. Secondly, the PK profile of the combination product was compared with mono-substance products (3 mg/kg subcutaneous and intramuscular). The PK profile of ketoprofen in the combination product was characterized by longer t_1/2 , lower C_max and increased AUC in comparison with mono-substance products. Due to prolonged ketoprofen exposure in the combination product, the pyrexia reducing effect of the combination product lasted longer in a second lipopolysaccharide challenge study in comparison with mono-substance products. Finally, a PK-PD model for the anti-pyretic effect of ketoprofen was developed based on the data from the different studies. The PK-PD model eliminated the need for additional animal experiments and indicated that a 3 mg/kg ketoprofen dose in the combination product provided optimal efficacy.

Population pharmacodynamic modeling of intramuscular and oral dexamethasone and betamethasone effects on six biomarkers with circadian complexities in Indian women

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.

Exposure-response modeling of peficitinib efficacy in patients with rheumatoid arthritis

The aim was to analyze the relationship between peficitinib exposure and efficacy response according to American College of Rheumatology (ACR) 20 criteria and 28‐joint disease activity score based on C‐reactive protein (DAS28‐CRP) in rheumatoid arthritis (RA) patients, and to identify relevant covariates by developing exposure–response models. The analysis incorporated results from three multicenter, placebo‐controlled, double‐blind studies. As an exposure parameter, individual post hoc pharmacokinetic (PK) parameters were obtained from a previously constructed population PK model. Longitudinal ACR20 response rate and individual longitudinal DAS28‐CRP measurements were modeled by a non‐linear mixed effect model. Influential covariates were explored, and their effects on efficacy were quantitatively assessed and compared. The exposure–response models of effect of peficitinib on duration‐dependent increase in ACR20 response rate and decrease in DAS28‐CRP were adequately described by a continuous time Markov model and an indirect response model, respectively, with a sigmoidal E_max saturable of drug exposure in RA patients. The significant covariates were DAS28‐CRP and total bilirubin at baseline for the ACR20 response model, and CRP at baseline and concomitant methotrexate treatment for the DAS28–CRP model. The covariate effects were highly consistent between the two models. Our exposure–response models of peficitinib in RA patients satisfactorily described duration‐dependent improvements in ACR20 response rates and DAS28‐CRP measurements, and provided consistent covariate effects. Only the ACR20 model incorporated a patient's subjective high expectations just after the start of the treatment. Therefore, due to their similarities and differences, both models may have relevant applications in the development of RA treatment.

Clinical trial registration

NCT01649999 (RAJ1), NCT02308163 (RAJ3), NCT02305849 (RAJ4).

Population Pharmacokinetic Model of N-acetylmannosamine (ManNAc) and N-acetylneuraminic acid (Neu5Ac) in Subjects with GNE Myopathy

BACKGROUND

GNE myopathy is a rare genetic muscle disease resulting from deficiency in an enzyme critical for the biosynthesis of N-acetylneuraminic acid (Neu5Ac, sialic acid). The uncharged Neu5Ac precursor, N-acetylmannosamine (ManNAc), is under development as an orphan drug for treating GNE myopathy.

METHODS

A semi-mechanistic population pharmacokinetic model was developed to simultaneously characterize plasma ManNAc and its metabolite Neu5Ac following oral administration of ManNAc to subjects with GNE myopathy. Plasma ManNAc and Neu5Ac pharmacokinetic data were obtained from two clinical studies (ClinicalTrials.gov identifiers NCT01634750, NCT02346461) and were simultaneously modeled using NONMEM.

RESULTS

ManNAc and Neu5Ac plasma concentrations were obtained from 34 subjects with GNE myopathy (16 male, 18 female, median age 39.5 years). The model parameter estimates included oral absorption rate (k_a) = 0.256 h^-1, relative bioavailability relationship with dose (F-Dose) slope = -0.405 (where F = 1 for 6-g dose), apparent clearance (CL_M/F) = 631 L/h, volume of distribution (V_M/F) = 506 L, Neu5Ac elimination rate constant (k_out) = 0.283 h^-1, initial ManNAc to Neu5Ac conversion (SLP₀) = 0.000619 (ng/mL)^-1 and at steady-state (SLP_SS) = 0.00334 (ng/mL)^-1, with a rate-constant of increase (k_inc) = 0.0287 h^-1. Goodness-of-fit plots demonstrated an acceptable and unbiased fit to the plasma ManNAc and Neu5Ac concentration data. Visual predictive checks demonstrated reasonable agreement between the 5th, 50th, and 95th percentiles of the observed and simulated data.

CONCLUSIONS

This population pharmacokinetic model can be used to evaluate ManNAc dosing regimens and to calculate Neu5Ac production and exposure following oral administration of ManNAc in subjects with GNE myopathy.

Safety, tolerability, pharmacokinetics, pharmacodynamics, bioavailability and food effect of single doses of soticlestat in healthy subjects

Aims

Soticlestat is a first‐in‐class selective inhibitor of cholesterol 24‐hydroxylase, the enzyme that converts brain cholesterol to 24S‐hydroxycholesterol (24HC), a positive allosteric modulator of N‐methyl‐D‐aspartate receptors. Soticlestat is under development as treatment for rare developmental and epileptic encephalopathies.

Methods

In this first‐in‐human study, 48 healthy men and women received single ascending doses of soticlestat oral solution or placebo. Subsequently, nine healthy subjects received soticlestat tablets under fed and fasting conditions to assess the relative oral bioavailability and effects of food. Serial blood and urine samples were collected for pharmacokinetic and pharmacodynamic assessments.

Results

Soticlestat appeared to be well tolerated up to a single dose of 1350 mg. Adverse events (AEs) were mild in intensity, and dose‐dependent increase in AE prevalence was not apparent. Soticlestat administered via oral solution was rapidly absorbed (median time to maximum plasma concentration [C_max] 0.250–0.520 h). Mean C_max and area under plasma concentration–time curve from zero to infinity increased by 183‐ and 581‐fold, respectively, over a 90‐fold dose increase. Mean terminal elimination half‐life was 0.820–7.16 hours across doses. Renal excretion was negligible. Administration of soticlestat tablets, and with food, lowered C_max but did not affect overall exposure. Plasma 24HC concentrations generally decreased with increasing dose.

Conclusions

Soticlestat appeared to be well tolerated after a single oral administration of up to 1350 mg and dose‐dependently reduced plasma 24HC concentrations. Systemic exposure increased in a greater than dose‐proportional manner over the dose range evaluated but was not affected by formulation or administration with food.

Optimal age and outcome measures for Alzheimer's disease prevention trials in people with Down syndrome

INTRODUCTION

People with Down syndrome (DS) typically develop Alzheimer's disease (AD) neuropathology before age 40, but a lack of outcome measures and longitudinal data have impeded their inclusion in randomized controlled trials (RCTs).

METHODS

Cohort study. Event-based and dose-response Emax models were fitted to longitudinal cognitive data, to stage AD and determine the earliest ages of decline. Results informed sample size estimations for hypothetical RCTs of disease-modifying treatments that reduced decline by 35% or 75%.

RESULTS

Seventy-five percent of participants progressed or remained stable in the AD staging model; effect sizes varied by age group and tests. Varied treatment effects could be detected with 50-200 people per arm when using sensitive cognitive outcome measures and targeting recruitment to ages 36 to 45 years.

DISCUSSION

Efficient RCTs of AD preventative treatments can be conducted in the DS population using sensitive outcome measures to monitor early decline. Dose-response models could help tailor future RCTs.

Relationship of the Calcitonin Gene-Related Peptide Monoclonal Antibody Galcanezumab Pharmacokinetics and Capsaicin-Induced Dermal Blood Flow in Healthy Subjects

Galcanezumab, a humanized monoclonal antibody targeting calcitonin gene-related peptide, was recently approved for migraine prophylaxis. The pharmacokinetic/pharmacodynamic (PK/PD) relationship between galcanezumab concentration and inhibition of capsaicin-induced dermal blood flow (CIDBF) was evaluated using first-in-human data following 6 single subcutaneous doses (1 to 600 mg) or multiple (4) 150-mg doses every 2 weeks in 7 cohorts (7 actively treated subjects and 2 placebo-treated healthy subjects). Galcanezumab pharmacokinetics were best described by a 1-compartment model with delayed first-order absorption/linear elimination. Apparent estimates (between-subject variability) of clearance, volume of distribution, absorption rate constant, and lag time were 0.0106 L/h (27%CV), 11.2 L (21%CV), 0.0192 h^-1 (89%CV), and 0.202 hours, respectively. Estimated elimination half-life was about 30 days. An effect compartment link model described the concentration-effect relationship; estimated maximum inhibitory effect was 70.5%, and 50% maximum inhibitory effect concentration (IC₅₀ ) was 1060 ng/mL. Galcanezumab showed dose- and concentration-dependent potent and durable inhibition of CIDBF. Simulated effect compartment concentrations were maintained above IC₅₀ after 12 weeks of dosing. Near-maximal CIDBF inhibition occurred with 150 mg biweekly for 12 weeks lasting ≥24 weeks or with ≥30 mg every 2 weeks or 195 mg every 13 weeks. Quantitative modeling of galcanezumab PK/PD supported dose selection for the phase 2 proof-of-concept study.

Accelerating Development of Benziamidazole-Class Proton Pump Inhibitors: A Mechanism-Based PK/PD Model to Optimize Study Design with Ilaprazole as a Case Drug

Proton pump inhibitors (PPIs) are the mainstay for treatment of acid-related diseases. This study developed a mechanism-based pharmacokinetic (PK) and pharmacodynamics (PD) model with ilaprazole as case drug, so as to support and accelerate the development of novel PPIs. The model was established and verified using the PK and PD data from 26 subjects receiving 5 to 30 mg of ilaprazole and 22 subjects receiving the loading dose of ilaprazole 20 mg followed by 10 mg once daily for 2 days. The nonlinear mixed-effects modeling approach was performed for the PK/PD model. A two-compartment model with linear elimination and covariates (body weight and gender) described the observed data well. The relationship between plasma concentrations of ilaprazole and gastric acid pH was well quantified with individual variability, in which the synthesis and degradation of H⁺/K⁺-ATPase, the food effect, the circular rhythms of gastric acid secretion, and the irreversible inhibition of H⁺/K⁺-ATPase by ilaprazole were integrated. This PK/PD model well predicted the PK and PD profile of ilaprazole in healthy subjects and patients with duodenal ulcers receiving wide range dose regimens. The mechanism-based PK/PD model provided a potential strategy to accelerate the development of novel PPIs by waiving the unnecessary clinical trials.

OptiDose: Computing the Individualized Optimal Drug Dosing Regimen Using Optimal Control

Providing the optimal dosing strategy of a drug for an individual patient is an important task in pharmaceutical sciences and daily clinical application. We developed and validated an optimal dosing algorithm (OptiDose) that computes the optimal individualized dosing regimen for pharmacokinetic-pharmacodynamic models in substantially different scenarios with various routes of administration by solving an optimal control problem. The aim is to compute a control that brings the underlying system as closely as possible to a desired reference function by minimizing a cost functional. In pharmacokinetic-pharmacodynamic modeling, the controls are the administered doses and the reference function can be the disease progression. Drug administration at certain time points provides a finite number of discrete controls, the drug doses, determining the drug concentration and its effect on the disease progression. Consequently, rewriting the cost functional gives a finite-dimensional optimal control problem depending only on the doses. Adjoint techniques allow to compute the gradient of the cost functional efficiently. This admits to solve the optimal control problem with robust algorithms such as quasi-Newton methods from finite-dimensional optimization. OptiDose is applied to three relevant but substantially different pharmacokinetic-pharmacodynamic examples.

Trilaciclib dose selection: an integrated pharmacokinetic and pharmacodynamic analysis of preclinical data and Phase Ib/IIa studies in patients with extensive-stage small cell lung cancer

Purpose

Trilaciclib is a first-in-class CDK4/6 inhibitor that transiently arrests hematopoietic stem and progenitor cells (HSPCs) in the G1 phase of the cell cycle to preserve them from chemotherapy-induced damage (myelopreservation). We report integrated analyses of preclinical and clinical data that informed selection of the recommended Phase II dose (RP2D) used in trilaciclib trials in extensive-stage small cell lung cancer (ES-SCLC).

Methods

A semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model developed from preclinical data guided selection of an optimal dose for G1 bone marrow arrest in a first-in-human Phase I study (G1T28-1-01). PK, PD, safety, and efficacy data from G1T28-1-01 and two Phase Ib/IIa studies (G1T28-02/-03) in ES-SCLC were analyzed to support RP2D selection.

Results

Model simulation of bone marrow arrest based on preclinical data predicted that a ≥ 192 mg/m² dose would induce a 40–50% decrease in total bone marrow proliferation in humans and almost 100% cell cycle arrest of cycling HSPCs. Consistent with this model, analysis of bone marrow aspirates in healthy volunteers after trilaciclib 192 mg/m² administration demonstrated almost 100% G1 arrest in HSPCs and 40% decrease in total bone marrow proliferation, with minimal toxicity. G1T28-02/-03 reported similar PK parameters with trilaciclib 200 mg/m² but slightly lower exposures than expected compared with healthy volunteers; consequently, 240 and 280 mg/m² doses were also tested to match healthy volunteer exposures. Based on PK and relevant safety data, 240 mg/m² was selected as the RP2D, which was also favored by myelopreservation endpoints in G1T28-02/-03.

Conclusion

Integrated PK/PD, safety, and efficacy data support 240 mg/m² as the RP2D for trilaciclib.

ClinicalTrials.gov Identifiers

NCT02243150; NCT02499770; NCT02514447.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-021-04239-9.

Multiscale and Translational Quantitative Systems Toxicology, Pharmacokinetic-Toxicodynamic Modeling Analysis for Assessment of Doxorubicin-Induced Cardiotoxicity

Dose-dependent life-threatening doxorubicin-induced cardiotoxicity (DIC) is a major clinical challenge that needs to be addressed. Here, we developed an integrated multiscale and translational quantitative systems toxicology and pharmacokinetic-toxicodynamic (QST-PK/TD) model for optimization of doxorubicin dosing regimens for early monitoring and minimization of DIC. A QST model was established by exposing human cardiomyocytes, AC16 cells, to doxorubicin over a time course, and measuring the dynamics of intracellular signaling proteins, AC16 cell viability and released biomarkers of cardiomyocyte injury such as the B-type natriuretic peptide (BNP). Experiments were scaled up to a three-dimensional and dynamic (3DD) cell culture system to evaluate DIC under various dosing regimens. The PK determinants of doxorubicin influencing DIC were identified in vitro and then translated to the in vivo setting through hybrid physiologically based PK (PBPK)/TD models using preclinical- and clinical-level data extracted from literature. The developed cellular-level QST model captured well the observed dynamics of intracellular proteins, AC16 cell viability and BNP kinetics. In the 3DD setting, dose fractionation of doxorubicin displayed a significant reduction in cardiotoxicity compared to single intravenous doses with equal exposure, implying doxorubicin peak concentrations as the PK determinant for DIC. The in vivo hybrid PBPK/TD models captured well doxorubicin PK and DIC. Peak doxorubicin concentrations correlated well with acute DIC for dose-fractionated regimens, while maximum 48-h moving average concentrations correlated with DIC for dose-fractionated and long-term infusion regimens in vivo. The developed multiscale and translational QST-PK/TD modeling platform may serve as an in silico tool for assessment of early toxicity and/or efficacy of developmental drugs in vitro.

A quantitative systems pharmacological approach identified activation of JNK signaling pathway as a promising treatment strategy for refractory HER2 positive breast cancer

HER2-positive breast cancer (BC) is a rapidly growing and aggressive BC subtype that predominantly affects younger women. Despite improvements in patient outcomes with anti-HER2 therapy, primary and/or acquired resistance remain a major clinical challenge. Here, we sought to use a quantitative systems pharmacological (QSP) approach to evaluate the efficacy of lapatinib (LAP), abemaciclib (ABE) and 5-fluorouracil (5-FU) mono- and combination therapies in JIMT-1 cells, a HER2+ BC cell line exhibiting intrinsic resistance to trastuzumab. Concentration-response relationships and temporal profiles of cellular viability were assessed upon exposure to single agents and their combinations. To quantify the nature and intensity of drug-drug interactions, pharmacodynamic cellular response models were generated, to characterize single agent and combination time course data. Temporal changes in cell-cycle phase distributions, intracellular protein signaling, and JIMT-1 cellular viability were quantified, and a systems-based protein signaling network model was developed, integrating  protein dynamics to drive the observed changes in cell viability. Global sensitivity analyses for each treatment arm were performed, to identify the most influential parameters governing cellular responses. Our QSP model was able to adequately characterize protein dynamic and cellular viability trends following single and combination drug exposure. Moreover, the model and subsequent sensitivity analyses suggest that the  activation of the stress pathway, through pJNK, has the greatest impact over the observed declines of JIMT-1 cell viability in vitro. These findings suggest that dual HER2 and CDK 4/6 inhibition may be a promising novel treatment strategy for refractory HER2+ BC, however, proof-of-concept in vivo studies are needed to further evaluate the combined use of these therapies.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

An integrated PK-PD model for cortisol and the 17-hydroxyprogesterone and androstenedione biomarkers in children with congenital adrenal hyperplasia

Aims

The aim of this study was to characterize the pharmacokinetic/pharmacodynamic relationships of cortisol and the adrenal biomarkers 17‐hydroxyprogesterone and androstenedione in children with congenital adrenal hyperplasia (CAH).

Methods

A nonlinear mixed‐effect modelling approach was used to analyse cortisol, 17‐hydroxyprogesterone and androstenedione concentrations obtained over 6 hours from children with CAH (n = 50). A circadian rhythm was evident and the model leveraged literature information on circadian rhythm in untreated children with CAH. Indirect response models were applied in which cortisol inhibited the production rate of all three compounds using an I_max model.

Results

Cortisol was characterized by a one‐compartment model with apparent clearance and volume of distribution estimated at 22.9 L/h/70 kg and 41.1 L/70 kg, respectively. The IC₅₀ values of cortisol concentrations for cortisol, 17‐hydroxyprogesterone and androstenedione were estimated to be 1.36, 0.45 and 0.75 μg/dL, respectively. The inhibitory effect was found to be more potent on 17OHP than D4A, and the IC₅₀ values were higher in salt‐wasting subjects than simple virilizers. Production rates of cortisol, 17‐hydroxyprogesterone and androstenedione were higher in simple‐virilizer subjects. Half‐lives of cortisol, 17‐hydroxyprogesterone and androstenedione were 60, 47 and 77 minutes, respectively.

Conclusion

Rapidly changing biomarker responses to cortisol concentrations highlight that single measurements provide volatile information about a child's disease control. Our model closely captured observed cortisol, 17‐hydroxyprogesterone and androstenedione concentrations. It can be used to predict concentrations over 24 hours and allows many novel exposure metrics to be calculated, e.g., AUC, AUC‐above‐threshold, time‐within‐range, etc. Our long‐range goal is to uncover dose–exposure–outcome relationships that clinicians can use in adjusting hydrocortisone dose and timing.

An Integrated TK-TD Model for Evaluation of Radix Aconitikusnezoffii (RAK)

OBJECTIVE

An integrated TK-TD model with indirect response to toxicity was established using ADAPT 5 to evaluate abnormal heart rate (HR) and QT interval changes caused by Radix Aconitikusnezoffii (RAK).

METHODS

Plasma samples were collected from male SD rats, which were divided into the blank and RAK groups. HR and QT interval indicators were recorded. Four alternative TK models were analyzed, and the best fitting model was determined. An indirect toxicodynamics model was selected, and the relationship of plasma concentration-time-toxicity was linked by Hill's equation.

RESULTS

A 1-compartment linear first-order elimination kinetic model with the biophase model - an indirect toxic effect response model - best described the data. The high-dose QT interval was evaluated. Model simulation with the ML method showed that the fitting values of 0-15 h all fell within the confidence interval (95%). AMOS analysis showed that almost all the load factor of the variable was >0.7, and the χ2 value was 4.169 indicating a significant difference. Load factor (correlation coefficient) between the HR and QT intervals was -0.965, indicating negative correlation.

CONCLUSIONS

The integrated TK-TD model with linear atrioventricular first-order elimination kinetics and indirect response represents a novel mathematical method to evaluate drug-induced changes in HR and QT.

Pharmacokinetic and pharmacodynamic modeling of the metastin/kisspeptin analog, TAK-448, for its anti-tumor efficacy in a rat xenograft model

TAK-448 is the investigational metastin/kisspeptin analog, which is known to have an anti-tumor effect through suppression of androgen hormones (luteinizing hormone and testosterone) levels. This study developed pharmacokinetic-pharmacodynamic (PK/PD) models of TAK-448 and leuprorelin acetate (TAP-144) in a rat vertebral-cancer of the prostate (VCaP) androgen-sensitive prostate cancer xenograft model to quantitatively assess and compare the anti-tumor effects of both drugs. A potential contribution of the hormone-independent direct effects of TAK-448 to the tumor growth inhibition was also investigated in the in vivo rat xenograft model, because our in vitro experiments revealed that TAK-448 may also directly suppress VCaP cellular proliferation. The PK/PD model successfully described the time course of tumor growth inhibition after drug treatment as well as the development of resistance to the inhibition of androgen hormones, following drug treatment or castration. The EC₅₀ of the hormone-dependent inhibitory effect of TAK-448 was much lower than that of TAP-144, and TAK-448 also has a faster onset of anti-tumor effect than TAP-144, demonstrating that TAK-448 has a stronger overall anti-tumor effect than TAP-144. In addition, model inference, by incorporating a hormone-independent inhibition pathway of TAK-448 into the PK-PD model, suggested that such a direct inhibition pathway for TAK-448 cannot be excluded, as also indicated by in vitro studies, but its EC₅₀ would be approximately three orders of magnitude higher than that of the hormone-dependent pathway. This study helps to understand the potential and mechanism of TAK-448 as a prostate cancer treatment.

Application of Pharmacokinetic-Pharmacodynamic Modeling in Drug Delivery: Development and Challenges

With the advancement of technology, drug delivery systems and molecules with more complex architecture are developed. As a result, the drug absorption and disposition processes after administration of these drug delivery systems and engineered molecules become exceedingly complex. As the pharmacokinetic and pharmacodynamic (PK-PD) modeling allows for the separation of the drug-, carrier- and pharmacological system-specific parameters, it has been widely used to improve understanding of the in vivo behavior of these complex delivery systems and help their development. In this review, we summarized the basic PK-PD modeling theory in drug delivery and demonstrated how it had been applied to help the development of new delivery systems and modified large molecules. The linkage between PK and PD was highlighted. In particular, we exemplified the application of PK-PD modeling in the development of extended-release formulations, liposomal drugs, modified proteins, and antibody-drug conjugates. Furthermore, the model-based simulation using primary PD models for direct and indirect PD responses was conducted to explain the assertion of hypothetical minimal effective concentration or threshold in the exposure-response relationship of many drugs and its misconception. The limitations and challenges of the mechanism-based PK-PD model were also discussed.

Packaging Design Using Mustard Seeds as a Natural Antimicrobial: A Study on Inhibition of Pseudomonas fragi in Liquid Medium

Pseudomonas fragi is the dominant spoilage organism in various foods, especially in spoiled milk, fish, and meats. Its growth can be inhibited by releasing allyl isothiocyanate (AITC) from ground mustard seeds in food packages. This paper aims to investigate the antimicrobial potential of ground mustard seeds against P. fragi growth and the effectiveness of released AITC concentration from mustard seeds on microbial inhibition of the spoilage bacteria growing in the liquid medium. The AITC concentration in the headspace and the liquid medium was measured and the growth of P. fragi in the liquid medium was monitored. Depending on the concentration of AITC, not only growth was inhibited but a reduction of the total count of P. fragi was even observed. The inactivation rate (k) of P. fragi was estimated using first-order inactivation kinetics and the minimum gaseous-released AITC to inactivate P. fragi was determined. Higher AITC concentration in the headspace and liquid medium was observed when using a higher amount of ground mustard seeds and a lower food to headspace ratio. Increasing the amount of ground mustard seeds (>100 mg per 10 mL liquid medium) led to full inactivation of P. fragi in 48 hours. By using an inhibition sigmoid Emax model, the minimum gaseous-released AITC for inactivation of P. fragi in 48 hours was observed around 15 µg/L headspace. These results indicate that inhibition of the spoilage bacteria and extending the shelf life using ground mustard seeds is only possible by applying a careful design of the packaging system.

Nangibotide in patients with septic shock: a Phase 2a randomized controlled clinical trial

PURPOSE

Nangibotide is a specific TREM-1 inhibitor that tempered deleterious host-pathogens interactions, restored vascular function, and improved survival, in animal septic shock models. This study evaluated the safety and pharmacokinetics of nangibotide and its effects on clinical and pharmacodynamic parameters in septic shock patients.

METHODS

This was a multicenter randomized, double-blind, two-stage study. Patients received either continuous infusion of nangibotide (0.3, 1.0, or 3.0 mg/kg/h) or placebo. Treatment began < 24 h after shock onset and continued for up to 5 days. Safety primary outcomes were adverse events (AEs), whether serious or not, and death. Exploratory endpoints evaluated nangibotide effects on pharmacodynamics, organ function, and mortality, and were analyzed according to baseline sTREM-1 concentrations.

RESULTS

Forty-nine patients were randomized. All treatment emergent AEs (TEAEs) were collected until Day 28. No significant differences were observed in TEAEs between treatment groups. No drug withdrawal linked to TEAE nor appearance of anti-drug antibodies were reported. Nangibotide pharmacokinetics appeared to be dose-proportional and clearance was dose-independent. Nangibotide did not significantly affect pharmacodynamic markers. Decrease in SOFA score LS mean change (± SE) from baseline to Day 5 in pooled nangibotide groups versus placebo was - 0.7 (± 0.85) in the randomized population and - 1.5 (± 1.12) in patients with high baseline plasma sTREM-1 concentrations (non-significant). This pattern was similar to organ support end points.

CONCLUSION

No significant increases in TEAEs were detected in nangibotide-treated patients versus placebo. These results encourage further evaluation of nangibotide and further exploration of plasma sTREM-1 concentrations as a predictive efficacy biomarker.

Pharmacokinetics and pharmacodynamics of three oral formulations of curcumin in rats

Curcumin (CUR) is a major component of turmeric Curcuma longa, which is often used in food or as a dietary supplement. The purpose of this preclinical study is to investigate the acute pharmacokinetic and pharmacodynamic (PK/PD) profiles of two commercially marketed CUR products (GNC and Vitamin Shoppe) and a CUR powder from Sigma in female rats. Plasma samples were collected at specific time points and analyzed for CUR and its metabolite curcumin-O-glucuronide. RNA was extracted from leukocytes and analyzed for the expression of Nrf2-mediated antioxidant genes Nrf2, Ho-1, and Nqo1 by qPCR as selected PD markers. CUR PK was characterized by a 2-compartment model (2CM) after intravenous (IV) or oral administrations. Compared to IV CUR, the absolute bioavailability (F) of CUR for GNC (GC) is 0.9%, Vitamin Shoppe (VC) is 0.6% and Sigma (SC) is 3.1%. Pharmacodynamically, all three formulations showed induction of antioxidant Nrf2, Ho-1 and Nqo1 gene expression in rat leucocytes. PK/PD modeling of CUR's effect on antioxidant gene expression was well captured by an indirect response model. Physiologically based PK modeling and simulation using GastroPlus described the observed PK data reasonably well. In summary, our current study shows that the absolute oral bioavailability of the parent CUR was very low for all three formulations. However, despite the low CUR plasma concentrations, all three oral CUR formulations displayed PD response in the induction of Nrf2-mediated antioxidant genes, suggesting the potential of oral CUR contributing to the overall health beneficial effects of oral CUR.

Pharmacokinetic-Pharmacodynamic-Efficacy Modeling of ONO-7579, a Novel Pan-Tropomyosin Receptor Kinase Inhibitor, in a Murine Xenograft Tumor Model

The orally available and novel small molecule ONO-7579 (N-{2-[4-(2-amino-5-chloropyridin-3-yl)phenoxy]pyrimidin-5-yl}-N'-[2-(methanesulfonyl)-5-(trifluoromethyl)phenyl]urea) is a highly potent and selective pan-tropomyosin receptor kinase (TRK) inhibitor. The objective of the present study was to characterize the pharmacokinetic (PK), pharmacodynamic (PD), and antitumor efficacy relationships of ONO-7579 in mice xenografted with a human colorectal cancer cell line, KM12 (harboring the tropomyosin 3 (TPM3) -neurotrophic tyrosine receptor kinase 1 fusion gene), via a PK/PD modeling approach. Plasma and tumor concentrations of ONO-7579, tumor levels of phosphorylated TPM3-TRKA (pTRKA), and tumor volumes in the murine model were measured with a single or multiple dose of ONO-7579 (0.06-0.60 mg/kg) administered once daily. The PK/PD/efficacy models were developed in a sequential manner. Changes in plasma concentrations of ONO-7579 were described with an oral one-compartment model. Tumor concentrations of ONO-7579 were higher than plasma concentrations, and changes in ONO-7579 tumor concentrations were described with an additional tumor compartment that had no influence on plasma concentrations. pTRKA in tumors was described with a direct E_max model, and the tumor ONO-7579 concentration causing 50% of the maximum effect was estimated to be 17.6 ng/g. In addition, a pTRKA-driven tumor growth inhibition model indicated that ONO-7579 started to sharply increase the antitumor effect at pTRKA inhibition rates >60% and required >91.5% to reduce tumors. In conclusion, the developed PK/PD/efficacy models revealed a "switch-like" relationship between pTRKA inhibition rate and antitumor effect in a murine KM12 xenograft model, demonstrating that pTRKA in tumors could serve as an effective biomarker for scheduling the dose regimen in early-stage clinical studies. SIGNIFICANCE STATEMENT: In recent years, clinical development of TRK inhibitors in patients with neurotrophic tyrosine receptor kinase fusion-positive solid tumors has been accelerated. This research found that phosphorylated TRKA was a useful biomarker for explaining the antitumor efficacy of TRK inhibitors using a pharmacokinetic/pharmacodynamic modeling approach in xenograft mice. This finding suggests a rational dosing regimen in early-stage clinical studies for ONO-7579 (N-{2-[4-(2-amino-5-chloropyridin-3-yl)phenoxy]pyrimidin-5-yl}-N'-[2-(methanesulfonyl)-5-(trifluoromethyl)phenyl]urea), a novel pan-TRK inhibitor.

Translational Pharmacokinetic-Pharmacodynamic Modeling for an Orally Available Novel Inhibitor of Epigenetic Regulator Enhancer of Zeste Homolog 2

PF06821497 has been identified as an orally available small-molecule enhancer of zeste homolog 2 inhibitor. The objectives of the present study were to characterize pharmacokinetic-pharmacodynamic-disease relationships of PF06821497 in xenograft mouse models with diffuse large B-cell lymphoma (Karpas422). An indirect-response model reasonably fit dose-dependent pharmacodynamic responses [histone H3 on lysine 27 (H3K27) me3 inhibition] with an unbound EC ₅₀ of 76 nM, whereas a signal-transduction model sufficiently fit dose-dependent disease responses (tumor growth inhibition) with an unbound tumor stasis concentration (T _sc ) of 168 nM. Thus, effective concentration for 70% of maximal effect (EC₇₀) for H3K27me3 inhibition was roughly comparable to T _sc , suggesting that 70% H3K27me3 inhibition could be required for tumor stasis. Consistently, an integrated pharmacokinetic-pharmacodynamic-disease model adequately describing tumor growth inhibition also suggested that ∼70% H3K27me3 inhibition was associated with tumor stasis. Based on these results, we would propose that an EC₇₀ estimate for H3K27me3 inhibition corresponding to tumor stasis could be considered a minimum target efficacious concentration of PF06821497 in cancer patients. SIGNIFICANCE STATEMENT: Using a mathematical modeling approach, the quantitative relationships of an orally available anticancer small-molecule enhancer of zeste homolog 2 inhibitor, PF06821497, were characterized among pharmacokinetics, pharmacodynamic biomarker inhibition, and disease responses in nonclinical xenograft models with diffuse large B-cell lymphoma. The modeling results suggest that >70% histone H3 on lysine 27 (H3K27) me3 inhibition would be required for tumor stasis (i.e., 100% tumor growth inhibition). Accordingly, we would propose that an effective concentration for 70% of maximal effect estimate for H3K27me3 inhibition could be considered a minimum target efficacious concentration of PF06821497 in cancer patients.

Entering the era of computationally driven drug development

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure-activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

Recent advances in physiologically based pharmacokinetic and pharmacodynamic models for anticancer nanomedicines

Nanoparticles (NPs) have distinct pharmacokinetic (PK) properties and can potentially improve the absorption, distribution, metabolism, and elimination (ADME) of small-molecule drugs loaded therein. Owing to the unwanted toxicities of anticancer agents in healthy organs and tissues, their precise delivery to the tumor is an essential requirement. There have been numerous advancements in the development of nanomedicines for cancer therapy. Physiologically based PK (PBPK) models serve as excellent tools for describing and predicting the ADME properties and the efficacy and toxicity of drugs, in combination with pharmacodynamic (PD) models. The recent preliminary application of these modeling approaches to NPs demonstrated their potential benefits in research and development processes relevant to the ADME and pharmacodynamics of NPs and nanomedicines. Here, we comprehensively review the pharmacokinetics of NPs, the developed PBPK models for anticancer NPs, and the developed PD model for anticancer agents.

Quantitative analysis of the effect of fraction of inspired oxygen on peripheral oxygen saturation in healthy volunteers

Background

The international organization for standardization (ISO) 80601-2-61 dictates that the accuracy of a pulse oximeter should be assessed by a controlled desaturation study. We aimed to characterize the relationship between the fraction of inspired oxygen (FiO₂) and peripheral oxygen saturation (SpO₂) using a turnover model by retrospectively analyzing the data obtained from previous controlled desaturation studies.

Materials and Methods

Each volunteer was placed in a semi-Fowler's position and connected to a breathing circuit to administer the hypoxic gas mixture containing medical air, oxygen, nitrogen, and carbon dioxide. Volunteers were exposed to various levels of induced hypoxia over 70–100% arterial oxygen saturation (SaO₂). The study period consisted of two rounds of hypoxia and the volunteers were maintained in room air between each round. FiO₂ and SpO₂ were recorded continuously during the study period. A population pharmacodynamic analysis was performed with the NONMEM VII level 4 (ICON Development Solutions, Ellicott City, MD, USA).

Results

In total, 2899 SpO₂ data points obtained from 20 volunteers were used to determine the pharmacodynamic characteristics. The pharmacodynamic parameters were as follows: k_out = 0.942 1/min, I_max = 0.802, IC₅₀ = 85.3%, γ = 27.3.

Conclusion

The changes in SpO₂ due to decreases in FiO₂ well explained by the turnover model with inhibitory function as a sigmoidal model.

Physiologically based toxicokinetic models and in silico predicted partition coefficients to estimate tetrachlorodibenzo-p-dioxin transfer from feed into growing pigs

Tetrachlorodibenzo-p-dioxin (TCDD) is a ubiquitous, toxic, persistent and bioaccumulative organic pollutant. TCDD can potentially enter the food chain through contaminated food of animal origin as a consequence of feed contamination. Prediction of the TCDD transfer from feed into animal products is thus important for human health risk assessment. Here, we develop several physiologically based toxicokinetic (PBTK) models of TCDD transfer from contaminated feed into growing pigs (Sus scrofa) exposed to doses ranging from 24.52 to 3269.25 ng of TCDD. We test the consequences of explicit dose-dependent absorption (DDA) versus the indirect effects of a self-induced liver metabolism (SIM). The DDA and SIM models showed similar fit to experimental data, although currently it is not possible to unequivocally make statement on a mechanistic preference. The performance of both toxicokinetic models was successfully evaluated using the 1999 Belgian case of contaminated fats for feeding. In combination with toxicokinetic models of other dioxin congeners, they can be used to formulate maximum allowance levels of dioxins in feedstuffs for pigs. Additionally, the implementation of in silico-predicted partition coefficients was explored as a useful alternative to predict TCDD tissue distribution in low-dose scenarios without recurring to animal experiments.

Comparing Model Performance in Characterizing the PK/PD of the Anti-Myostatin Antibody Domagrozumab

Modeling and simulation provides quantitative information on target coverage for dose selection. Optimal model selection often relies on fit criteria and is not necessarily mechanistically driven. One such case is discussed where healthy volunteer data of an anti‐myostatin monoclonal antibody domagrozumab were used to develop different target‐mediated drug disposition models; a quasi‐steady state (QSS) rapid binding approximation model, a Michaelis−Menten (MM)‐binding kinetics (MM‐BK) model, and an MM‐indirect response (MM‐IDR) model. Whereas the MM‐BK model was identified as optimal in fitting the data, with all parameters estimated with high precision, the QSS model also converged but was not able to capture the nonlinear decline. Although the least mechanistic model, MM‐IDR, had the lowest objective function value, the MM‐BK model was further developed as it provided a reasonable fit and allowed simulations regarding growth differentiation factor‐8 target coverage for phase II dose selection with sufficient certainty to allow for testing of the underlying mechanistic assumptions.

Impact of Antibiotic Gut Exposure on the Temporal Changes in Microbiome Diversity

Although the global deleterious impact of antibiotics on the intestinal microbiota is well known, temporal changes in microbial diversity during and after an antibiotic treatment are still poorly characterized. We used plasma and fecal samples collected frequently during treatment and up to one month after from 22 healthy volunteers assigned to a 5-day treatment by moxifloxacin (n = 14) or no intervention (n = 8). Moxifloxacin concentrations were measured in both plasma and feces, and bacterial diversity was determined in feces by 16S rRNA gene profiling and quantified using the Shannon index and number of operational taxonomic units (OTUs). Nonlinear mixed effect models were used to relate drug pharmacokinetics and bacterial diversity over time. Moxifloxacin reduced bacterial diversity in a concentration-dependent manner, with a median maximal loss of 27.5% of the Shannon index (minimum [min], 17.5; maximum [max], 27.7) and 47.4% of the number of OTUs (min, 30.4; max, 48.3). As a consequence of both the long fecal half-life of moxifloxacin and the susceptibility of the gut microbiota to moxifloxacin, bacterial diversity indices did not return to their pretreatment levels until days 16 and 21, respectively. Finally, the model characterized the effect of moxifloxacin on bacterial diversity biomarkers and provides a novel framework for analyzing antibiotic effects on the intestinal microbiome.

Modeling Combined Anti-Inflammatory Effects of Dexamethasone and Tofacitinib in Arthritic Rats

Tofacitinib (TOF), a Janus kinase (JAK) inhibitor, which was approved in 2012, has been recommended for the treatment of clinically active rheumatoid arthritis (RA). Dexamethasone (DEX), a potent corticosteroid, is also used in RA therapy but with limited usefulness due to dose- and time-dependent adverse effects. This pilot study examines the single and combined effects of DEX and TOF in order to explore the steroid-sparing potential of TOF. Collagen-induced arthritic (CIA) rats were subcutaneously (SC) dosed with vehicle, 1.5 mg/kg TOF, 5 mg/kg TOF, 0.225 mg/kg DEX, or a combination of 1.5 mg/kg TOF and 0.225 mg/kg DEX. Paw sizes were measured as an index of disease and drug efficacy and dynamically depicted using a logistic function for natural paw growth, a turnover model for disease progression, an indirect response model for inhibitory effects of TOF and DEX and a non-competitive interaction model for the combined effect of DEX and TOF. TOF alone exerted only a slight inhibitory effect on RA paw edema compared to DEX, which reduced edema by 40%. In combination, TOF and DEX had additive effects with an interaction factor of 0.76. Using model simulations, a single SC dose of TOF does not have a visible steroid-sparing potential, although BID oral dosing has such potential. The current study suggests an additive effect of TOF and DEX and simulations indicate that further exploration of TOF and DEX administration timing may produce desirable drug efficacy with lower DEX doses.

Pharmacokinetics, Pharmacodynamics, and PKPD Modeling of Curcumin in Regulating Antioxidant and Epigenetic Gene Expression in Healthy Human Volunteers

Curcumin is a major component of the spice turmeric ( Curcuma longa), often used in food or as a dietary supplement. Many preclinical studies on curcumin suggest health benefits in many diseases due to its antioxidant/anti-inflammatory and epigenetic effects. The few human studies and curcumin's unfavorable pharmacokinetics (PK) have limited its potential, leading researchers to study and develop formulations to improve its PK. The purpose of this clinical study is to describe the acute pharmacokinetics and pharmacodynamics (PK/PD) of commercially marketed curcumin in normal, healthy human volunteers. Twelve volunteers received a 4 g dose of curcumin capsules with a standard breakfast. Plasma samples were collected at specified time points and analyzed for curcumin and its glucuronide levels. RNA was extracted from leukocytes and analyzed for expression of select antioxidant and epigenetic histone deacetylase (HDAC) genes. Plasma levels of parent curcumin were below the detection limit by HPLC-ITMS/MS/MS. However, curcumin-O-glucuronide (COG), a major metabolite of curcumin, was detected as soon as 30 min. These observations of little to no curcumin and some levels of metabolite are in line with previous studies. PD marker antioxidant genes NRF2, HO-1, and NQO1 and epigenetic genes HDAC1, HDAC2, HDAC3, and HDAC4 were quantified by qPCR. COG PK is well-described by a one-compartment model, and the PK/PD of COG and its effect on antioxidant and epigenetic gene expression are captured by an indirect response model (IDR). A structural population PK model was sequentially established using a nonlinear mixed-effect model program (Monolix Lixoft, Orsay, France). Physiologically based pharmacokinetic modeling (PBPK) and simulation using Simcyp correlated well with the observed data. Taken together, these results show that the bioavailability of the parent curcumin compound is low, and oral administration of curcumin can still deliver detectable levels of curcumin glucuronide metabolite. But most importantly, it elicits antioxidant and epigenetic effects which could contribute to the overall health beneficial effects of curcumin.

Population Pharmacokinetic and Pharmacodynamic Analysis of GLPG1690, an Autotaxin Inhibitor, in Healthy Volunteers and Patients with Idiopathic Pulmonary Fibrosis

BACKGROUND AND OBJECTIVES

GLPG1690 is an autotaxin inhibitor in development for the treatment of idiopathic pulmonary fibrosis. Several publications suggested a role of autotaxin in the control of disease-affected lung function and of lysophosphatidic acid in lung remodeling processes. The aim of the current article was to describe the exposure-response relationship of GLPG1690 and further develop a rational basis to support dose selection for clinical trials in patients with idiopathic pulmonary fibrosis.

METHODS

Two trials were conducted in healthy volunteers: in the first trial, GLPG1690 was administered as single doses from 20 mg up to 1500 mg, and subsequently in multiple daily doses of 300-1000 mg. In a second trial, the interaction of rifampin with 600 mg of GLPG1690 was evaluated. A third trial was conducted in patients with idiopathic pulmonary fibrosis administered 600 mg of GLPG1690 once daily for 12 weeks. The exposure-response (lysophosphatidic acid C18:2 reduction) relationship of GLPG1690 was first described using non-linear mixed-effects modeling and the model was subsequently deployed to simulate a lysophosphatidic acid C18:2 reduction as a biomarker of autotaxin inhibition in the dose range from 50 to 1000 mg once or twice daily.

RESULTS

The population pharmacokinetics and lysophosphatidic acid C18:2 response of GLPG1690 were adequately described by a combined population pharmacokinetic and pharmacokinetic/pharmacodynamic model. Dose, formulation, rifampin co-administration, health status (healthy volunteer vs. patient with idiopathic pulmonary fibrosis), and baseline lysophosphatidic acid C18:2 were identified as covariates in the model. The effect of dose on systemic clearance indicated that GLPG1690 followed a more than dose-proportional increase in exposure over the simulated dose range of 50-1000 mg once daily. Model-based simulations showed reductions in lysophosphatidic acid C18:2 of at least 80% with doses greater or equal to 200 mg once daily.

CONCLUSION

Based on these results, 200 and 600 mg once-daily doses were selected for future clinical trials in patients with idiopathic pulmonary fibrosis.

Population pharmacokinetic-pharmacodynamic modelling of the relationship between testosterone and prostate specific antigen in patients with prostate cancer during treatment with leuprorelin

AIMS

This investigation aimed to quantitatively characterize the relationship between the gonadotropin-releasing hormone agonist leuprorelin, testosterone (T) and prostate specific antigen (PSA) concentrations over time, to aid identification of a target T concentration that optimises the balance of the benefits of T suppression whilst reducing the risk of side effects related to futile over-suppression.

METHODS

Data from a single dose study to investigate the effect of leuprorelin in a 6-month depot formulation on T and PSA in prostate cancer patients were analysed using a population pharmacokinetic-pharmacodynamic modelling approach. The developed model was qualified using external data from 3 studies, in which the effect of different formulations of leuprorelin on T and PSA was evaluated in prostate cancer patients.

RESULTS

The effect of leuprorelin on the relationship between T and PSA was adequately characterized by the Romero model with minor modifications, combined with a turnover model to describe the delay in response between T and PSA. The data were significantly better described when assuming a minimum PSA level that is independent on the treatment-related reduction in T, as compared to a model with a proportional reduction in PSA and T.

CONCLUSIONS

The model-based analysis suggests that on a population level, reducing T concentrations below 35 ng/dL does not result in a further decrease in PSA levels (>95% of the minimal PSA level is reached). More data are required to support this relationship in the lower T and PSA range.

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK-PD) framework. While, at first sight, a Bayesian PK-PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often-overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.

Therapeutic targeting of the NRF2 and KEAP1 partnership in chronic diseases

The transcription factor NF-E2 p45-related factor 2 (NRF2; encoded by NFE2L2) and its principal negative regulator, the E3 ligase adaptor Kelch-like ECH-associated protein 1 (KEAP1), are critical in the maintenance of redox, metabolic and protein homeostasis, as well as the regulation of inflammation. Thus, NRF2 activation provides cytoprotection against numerous pathologies including chronic diseases of the lung and liver; autoimmune, neurodegenerative and metabolic disorders; and cancer initiation. One NRF2 activator has received clinical approval and several electrophilic modifiers of the cysteine-based sensor KEAP1 and inhibitors of its interaction with NRF2 are now in clinical development. However, challenges regarding target specificity, pharmacodynamic properties, efficacy and safety remain.

Population Pharmacokinetics and Pharmacodynamics of Teicoplanin and C-Reactive Protein in Hospitalized Patients With Gram-Positive Infections

Teicoplanin is an antibiotic agent used for the treatment of Gram-positive infections. The clinical benefit of teicoplanin is associated with its blood concentrations, but the optimal dosing regimen is not yet known. To explore the optimal individual dosing regimen, we performed a population pharmacokinetic (PK) and pharmacodynamic (PD) analysis targeting a large-scale population, including patients with a wide range of ages, body weights, and renal functions. The PK of teicoplanin was described with a 2-compartment model, and the PD of C-reactive protein (CRP) concentrations was described with a turnover maximum inhibition model. The elimination half-life of teicoplanin calculated from the final estimated parameters was 169 hours, and renal function was a significant covariate of teicoplanin clearance. The teicoplanin concentration producing 50% of the maximum inhibition of CRP production was estimated to be 2.66 mg/L. The minimum concentration of teicoplanin in patients with higher loading doses (15 mg/kg) reached the target range (15-30 mg/L) with a probability of >50% in the dosing simulation. We described the influence of body size, body composition, and renal function on the PK of teicoplanin. The population PKPD model of teicoplanin and CRP in this study should provide useful information for development of a dosing strategy including the sequential clinical benefit of teicoplanin.

Identification and Preclinical Evaluation of the Bicyclic Pyrimidine γ-Secretase Modulator BMS-932481

A triazine hit identified from a screen of the BMS compound collection was optimized for potency, in vivo activity, and off-target profile to produce the bicyclic pyrimidine γ-secretase modulator BMS-932481. The compound showed robust reductions of Aβ_1-42 and Aβ_1-40 in the plasma, brain, and cerebrospinal fluid of mice and rats. Consistent with the γ-secretase modulator mechanism, increases in Aβ_1-37 and Aβ_1-38 were observed, with no change in the total amount of Aβ_1-x produced. No Notch-based toxicity was observed, and the overall preclinical profile of BMS-932481 supported its further evaluation in human clinical trials.

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.

Indirect pharmacodynamic models for responses with circadian removal

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 [Formula: see text] or a periodic time-dependent loss constant [Formula: see text]. If either [Formula: see text] or [Formula: see text] 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 [Formula: see text] 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 [Formula: see text] 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).

Pharmacodynamic Models of Differential Bortezomib Signaling Across Several Cell Lines of Multiple Myeloma

The heterogeneous polyclonal nature of multiple myeloma complicates the identification of protein biomarkers predictive of drug response. In this study, a pharmacodynamic systems modeling approach was used to link in vitro bortezomib exposure and myeloma cell death. The exposure‐response was integrated through a network of important protein biomarker dynamics activated by bortezomib in four myeloma cell lines. The pharmacodynamic models reasonably characterized the protein and myeloma cell dynamics simultaneously following bortezomib (20 nM) treatment. The models were used to identify differences in pathway dynamics across cell lines from model‐estimated protein biomarker turnover parameters and global sensitivity analyses. Additionally, a statistical correlation analysis between drug sensitivity and model‐fitted protein activation profiles (i.e., cumulative area under the protein expression‐time curves) supported the identification of shared biomarkers associated with sensitivity differences among the cell lines. Both types of analysis identified similar important proteins associated with bortezomib pharmacodynamics, such as phosphorylated Nuclear Factor kappa‐light‐chain‐enhancer of activated B cells (pNFkappaB), phosphorylated protein kinase B (pAKT), and caspase‐8 (Cas 8).

Pharmacokinetics and Pharmacodynamics of Curcumin in regulating anti-inflammatory and epigenetic gene expression

Chronic inflammation is a key driver of cancer development. Nitrite levels, which are regulated by inducible nitric oxide synthase (iNOS), play a critical role in inflammation. While the anti-oxidant and anti-inflammatory effects of curcumin, a natural product present in the roots of Curcuma longa have been studied widely, the acute pharmacokinetics (PK) and pharmacodynamics (PD) of curcumin in suppressing pro-inflammatory markers and epigenetic modulators remain unclear. This study evaluated the PK and PD of curcumin-induced suppression of lipopolysaccharide (LPS)-mediated inflammation in rat lymphocytes. LPS was administered intravenously either alone or with curcumin to female Sprague-Dawley rats. Plasma samples were analysed for curcumin concentration and mRNA expression was quantified in lymphocytes. The relative gene expression of several inflammatory and epigenetic modulators was analysed. To investigate the relationship between curcumin concentration and iNOS, TNF-α, and IL-6 gene expression, PK/PD modeling using Jusko's indirect response model (IDR) integrating transit compartments (TC) describing the delayed response was conducted. The concentration-time profile of curcumin exhibited a bi-exponential decline, which was well described by a two-compartmental pharmacokinetic model. Importantly the results demonstrate that LPS induced gene expression of pro-inflammatory markers in lymphocytes, with peak expression at approximately 3 h and curcumin suppressed the gene expression in animals administered with LPS. These effects were well captured using the IDR model and an IDR model with the transit compartments. In summary, the PK/PD modeling approach could potentially provide a robust quantitative framework for evaluating the acute anti-inflammatory and epigenetic effects of curcumin in future clinical trials.