Search

Clinical studies in healthy volunteers challenged with lipopolysaccharide (LPS), a constituent of the cell wall of Gram-negative bacteria, represent a key model to characterize the Toll-like receptor 4 (TLR4)-mediated inflammatory response. Here, we developed a mathematical modelling framework to quantitatively characterize the dynamics and inter-individual variability of multiple inflammatory biomarkers in healthy volunteer LPS challenge studies. Data from previously reported LPS challenge studies were used, which included individual-level time-course data for tumour necrosis factor alpha (TNF-alpha), interleukin 6 (IL-6), interleukin 8 (IL-8) and C-reactive protein (CRP). A one-compartment model with first-order elimination was used to capture the LPS kinetics. The relationships between LPS and inflammatory markers was characterized using indirect response (IDR) models. Delay differential equations were applied to quantify the delays in biomarker response profiles. For LPS kinetics, our estimates of clearance and volume of distribution were 35.7 L h(-1) and 6.35 L, respectively. Our model adequately captured the dynamics of multiple inflammatory biomarkers. The time delay for the secretion of TNF-alpha, IL-6 and IL-8 were estimated to be 0.924, 1.46 and 1.48 h, respectively. A second IDR model was used to describe the induced changes of CRP in relation to IL-6, with a delayed time of 4.2 h. The quantitative models developed in this study can be used to inform design of clinical LPS challenge studies and may help to translate preclinical LPS challenge studies to humans.

Changes that accompany older age can alter the pharmacokinetics (PK), pharmacodynamics (PD), and likelihood of adverse effects (AEs) of a drug. However, older adults, especially the oldest or those with multiple chronic health conditions, polypharmacy, or frailty, are often under-represented in clinical trials of new drugs. Deficits in the current conduct of clinical evaluation of drugs for older adults and potential steps to fill those knowledge gaps are presented in this communication. The most important step is to increase clinical trial enrollment of older adults who are representative of the target treatment population. Unnecessary eligibility criteria should be eliminated. Physical and financial barriers to participation should be removed. Incentives could be created for inclusion of older adults. Enrollment goals should be established based on intended treatment indications, prevalence of the condition, and feasibility. Relevant clinical pharmacology data need to be obtained early enough to guide dosing and reduce risk for participation of older adults. Relevant PK and PD data as well as patient-centered outcomes should be measured during trials. Trial data should be analyzed for differences in PK, PD, effectiveness, and safety arising from differences in age or from the presence of conditions common in older adults. Postmarket evaluations with real-world evidence and drug labeling updates throughout the product lifecycle reflecting new knowledge are also needed. A comprehensive plan is needed to ensure adequate evaluation of the safety and effectiveness of drugs in older adults.

There are several antibody therapeutics in preclinical and clinical development, industry-wide, for the treatment of central nervous system (CNS) disorders. Due to the limited permeability of antibodies across brain barriers, the quantitative understanding of antibody exposure in the CNS is important for the design of antibody drug characteristics and determining appropriate dosing regimens. We have developed a minimal physiologically-based pharmacokinetic (mPBPK) model of the brain for antibody therapeutics, which was reduced from an existing multi-species platform brain PBPK model. All non-brain compartments were combined into a single tissue compartment and cerebral spinal fluid (CSF) compartments were combined into a single CSF compartment. The mPBPK model contains 16 differential equations, compared to 100 in the original PBPK model, and improved simulation speed approximately 11-fold. Area under the curve ratios for minimal versus full PBPK models were close to 1 across species for both brain and plasma compartments, which indicates the reduced model simulations are similar to those of the original model. The minimal model retained detailed physiological processes of the brain while not significantly affecting model predictability, which supports the law of parsimony in the context of balancing model complexity with added predictive power. The minimal model has a variety of applications for supporting the preclinical development of antibody therapeutics and can be expanded to include target information for evaluating target engagement to inform clinical dose selection. Supplementary Information The online version contains supplementary material available at 10.1007/s10928-021-09776-7.

Prediction of antimicrobial target-site pharmacokinetics is of relevance to optimize treatment with antimicrobial agents. A physiologically based pharmacokinetic (PBPK) model framework was developed for prediction of pulmonary pharmacokinetics, including key pulmonary infection sites (i.e. the alveolar macrophages and the epithelial lining fluid).\nThe modelling framework incorporated three lung PBPK models: a general passive permeability-limited model, a drug-specific permeability-limited model and a quantitative structure-property relationship (QSPR)-informed perfusion-limited model. We applied the modelling framework to three fluoroquinolone antibiotics. Incorporation of experimental drug-specific permeability data was found essential for accurate prediction.\nIn the absence of drug-specific transport data, our QSPR-based model has generic applicability. Furthermore, we evaluated the impact of drug properties and pathophysiologically related changes on pulmonary pharmacokinetics. Pulmonary pharmacokinetics were highly affected by physiological changes, causing a shift in the main route of diffusion (i.e. paracellular or transcellular). Finally, we show that lysosomal trapping can cause an overestimation of cytosolic concentrations for basic compounds when measuring drug concentrations in cell homogenate.\nThe developed lung PBPK model framework constitutes a promising tool for characterization of pulmonary exposure of systemically administrated antimicrobials.

BackgroundQuantitative systems pharmacology (QSP) modeling of the host-immune response against Mtb can inform rational design of host-directed therapies (HDTs). We aimed to develop a QSP framework to evaluate the effects of metformin-associated autophagy-induction in combination with antibiotics.MethodsA QSP framework for autophagy was developed by extending a model for host-immune response to include AMPK-mTOR-autophagy signalling. This model was combined with pharmacokinetic-pharmacodynamic models for metformin and antibiotics against Mtb. We compared the model predictions to mice infection experiments, and derived predictions for pathogen and host-associated dynamics in humans treated with metformin in combination with antibiotics.ResultsThe model adequately captured the observed bacterial load dynamics in mice Mtb infection models treated with metformin. Simulations for adjunctive metformin therapy in newly diagnosed patients suggested a limited yet dose-dependent effect of metformin on reducing the intracellular bacterial load and selected pro-inflammatory cytokines. Our predictions suggest that metformin may provide beneficiary effects when overall bacterial load, or extracellular-to-intracellular bacterial ratio is low, either early after infection or late during antibiotic treatment.ConclusionsWe present the first QSP framework for HDTs against Mtb, linking cellular-level autophagy effects to disease progression. This framework may be extended to guide design of HDTs against Mtb.

Sepsis is a life-threatening condition driven by the dysregulation of the host immune response to an infection. The complex and interacting mechanisms underlying sepsis remain not fully understood. By integrating prior knowledge from literature using mathematical modelling techniques, we aimed to obtain a deeper mechanistic insight into sepsis pathogenesis and to evaluate promising novel therapeutic targets, with a focus on Toll-like receptor 4 (TLR4)-mediated pathways. A Boolean network of regulatory relationships was developed for key immune components associated with sepsis pathogenesis after TLR4 activation. Perturbation analyses were conducted to identify therapeutic targets associated with organ dysfunction or antibacterial activity. The developed model consisted of 42 nodes and 183 interactions. Perturbation analyses suggest that over-expression of tumour necrosis factor alpha (TNF-α) or inhibition of soluble receptor sTNF-R, tissue factor, and inflammatory cytokines (IFN-γ, IL-12) may lead to a reduced activation of organ dysfunction related endpoints. Over-expression of complement factor C3b and C5b led to an increase in the bacterial clearance related endpoint. We identified that combinatory blockade of IFN-γ and IL-10 may reduce the risk of organ dysfunction. Finally, we found that combining antibiotic treatment with IL-1β targeted therapy may have the potential to decrease thrombosis. In summary, we demonstrate how existing biological knowledge can be effectively integrated using Boolean network analysis for hypothesis generation of potential treatment strategies and characterization of biomarker responses associated with the early inflammatory response in sepsis.

A phase 1 clinical trial in healthy male volunteers was conducted with a somatostatin-dopamine chimera (BIM23B065), from which information could be obtained on the concentration-effect relationship of the inhibition of pulsatile endogenous growth hormone and prolactin secretion. Endogenous growth hormone profiles were analyzed using a two-step deconvolution-analysis-informed population pharmacodynamic modeling approach, which was developed for the analyses of pulsatile profiles. Prolactin concentrations were modelled using a population pool model with a circadian component on the prolactin release. During treatment with BIM23B065, growth hormone secretion was significantly reduced (maximal effect [EMAX] = − 64.8%) with significant reductions in the pulse frequency in two out of three multiple ascending dose cohorts. A circadian component in prolactin secretion was identified, modelled using a combination of two cosine functions with 24 h and 12 h periods. Dosing of BIM23B065 strongly inhibited (EMAX = − 91%) the prolactin release and demonstrated further reduction of prolactin secretion after multiple days of dosing. This study quantified the concentration-effect relationship of BIM23B065 on the release of two pituitary hormones, providing proof of pharmacology of the chimeric actions of BIM23B065. Electronic supplementary material The online version of this article (10.1007/s10928-020-09683-3) contains supplementary material, which is available to authorized users.

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These “omics” data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno‐oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non‐small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP.

Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker‐based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL‐6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker‐based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient‐based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis.

Zebrafish larvae are increasingly used for pharmacological research, but internal drug exposure is often not measured. Understanding pharmacokinetics is necessary for reliable translation of pharmacological results to higher vertebrates, including humans. Quantification of drug clearance and distribution requires measurements of blood concentrations. Additionally, measuring drug metabolites is of importance to understand clearance in this model organism mechanistically. We therefore mechanistically study and quantify pharmacokinetics in zebrafish larvae, and compare this to higher vertebrates, using paracetamol (acetaminophen) as paradigm compound. A method was developed to sample blood from zebrafish larvae at five days post fertilization. Blood concentrations of paracetamol and its major metabolites, paracetamol-glucuronide and paracetamol-sulphate, were measured. Blood concentration data were combined with measured amounts in larval homogenates and excreted amounts and simultaneously analysed through non-linear mixed effects modelling, quantifying absolute clearance and distribution volume. Blood sampling from zebrafish larvae was most successful from the posterior cardinal vein with median volume (interquartile range) of 1.12 (0.676-1.66) nL per blood sample. Samples were pooled (n=15-35) to reach measurable levels. Paracetamol blood concentrations at steady state were only 10% of the external paracetamol concentration. Paracetamol-sulphate was the major metabolite and its formation was quantified using a time-dependent metabolic formation rate. Absolute clearance and distribution volume correlated well to reported values in higher vertebrates, including humans. Based on blood concentrations and advanced data analysis, the mechanistic and quantitative understanding of paracetamol pharmacokinetics in zebrafish larvae has been established. This will improve the translational value of this vertebrate model organism in drug discovery and development.

There is an inherent tension in Quantitative Systems Pharmacology (QSP) between the need to incorporate mathematical descriptions of complex physiology and drug targets with the necessity of developing robust, predictive and well-constrained models. In addition to this, there is no “gold standard” for model development and assessment in QSP. Moreover, there can be confusion over terminology such as model and parameter identifiability; complex and simple models; virtual populations; and other concepts, which leads to potential miscommunication and misapplication of methodologies within modeling communities, both the QSP community and related disciplines. This perspective article highlights the pros and cons of using simple (often identifiable) vs. complex (more physiologically detailed but often non-identifiable) models, as well as aspects of parameter identifiability, sensitivity and inference methodologies for model development and analysis. The paper distills the central themes of the issue of identifiability and optimal model size and discusses open challenges.

Aims: Growth hormone (GH) secretion is pulsatile and secretion varies highly between individuals. To understand and ultimately predict GH secretion, it is important to first delineate and quantify the interaction and variability in the biological processes underlying stimulated GH secretion. This study reports on the development of a population nonlinear mixed effects model for GH stimulation, incorporating individual GH kinetics and the stimulation of GH by GH-releasing hormone (GHRH). Methods: Literature data on the systemic circulation, the median eminence, and the anterior pituitary were included as system parameters in the model. Population parameters were estimated on data from 8 healthy normal weight and 16 obese women who received a 33 μg recombinant human GH dose. The next day, a bolus injection of 100 μg GHRH was given to stimulate GH secretion. Results: The GH kinetics were best described with the addition of 2 distribution compartments with a bodyweight dependent clearance (increasing linearly from 24.7 L/h for a 60-kg subject to 32.1 L/h for a 100-kg subject). The model described the data adequately with high parameter precision and significant interindividual variability on the GH clearance and distribution volume. Additionally, high variability in the amount of secreted GH, driven by GHRH receptor activation, was identified (coefficient of variation = 90%). Conclusion: The stimulation of GH by GHRH was quantified and significant interindividual variability was identified on multiple parameters. This model sets the stage for further development of by inclusion of additional physiological components to quantify GH secretion in humans.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

Background and Purpose There is a clear need for innovation in anti-tuberculosis drug development. The zebrafish larva is an attractive disease model in tuberculosis research. To translate pharmacological findings to higher vertebrates, including humans, the internal exposure of drugs needs to be quantified and linked to observed response.Experimental Approach In zebrafish studies, drugs are usually dissolved in the external water, posing a challenge to quantify internal exposure. We developed experimental methods to quantify internal exposure, including nanoscale blood sampling, and to quantify the bacterial burden, using automated fluorescence imaging analysis, with isoniazid as the test compound. We used pharmacokinetic-pharmacodynamic modelling to quantify the exposure-response relationship responsible for the antibiotic response. To translate isoniazid response to humans, quantitative exposure-response relationships in zebrafish were linked to simulated concentration-time profiles in humans, and two quantitative translational factors on sensitivity to isoniazid and stage of infection were included.Key Results Blood concentration was only 20% of the external drug concentration. The bacterial burden increased exponentially, and an isoniazid dose corresponding to 15 mg center dot L(-1)internal concentration (minimum inhibitory concentration) leads to bacteriostasis of the mycobacterial infection in the zebrafish. The concentration-effect relationship was quantified, and based on that relationship and the translational factors, the isoniazid response was translated to humans, which correlated well with observed data.Conclusions and Implications This proof of concept study confirmed the potential of zebrafish larvae as tuberculosis disease models in translational pharmacology and contributes to innovative anti-tuberculosis drug development, which is very clearly needed.

The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP. Electronic supplementary material The online version of this article (10.1007/s11538-020-00708-z) contains supplementary material, which is available to authorized users.

and the cAMP turnover.\n .\n receptor antagonists, it has been hypothesized that fast receptor binding kinetics cause fewer side effects, because part of the dynamics of the dopaminergic system is preserved by displacement of these antagonists.\n receptor antagonist exposure were measured in vitro. These data were integrated by mechanistic modelling, taking into account competitive binding of endogenous dopamine and the antagonist, the turnover of the second messenger cAMP and negative feedback by PDE turnover.\nCONCLUSIONS AND IMPLICATIONS\nKEY RESULTS\nBACKGROUND AND PURPOSE\nEXPERIMENTAL APPROACH

receptor activation was evaluated using quinpirole as a paradigm compound. ), as well as plasma concentrations of 13 hormones and neuropeptides, were measured. Experiments were performed at day 1 and repeated after 7-day s.c. drug administration. PK/PD modelling was applied to identify the in vivo concentration-effect relations and neuroendocrine dynamics. receptor expression levels on the pituitary hormone-releasing cells predicted the concentration-effect relationship differences. Baseline levels (ACTH, prolactin, TSH), hormone release (ACTH) and potency (TSH) changed with treatment duration. agonists in clinical practice. Further development towards quantitative systems pharmacology models will eventually facilitate mechanistic drug development. BACKGROUND AND PURPOSE EXPERIMENTAL APPROACH KEY RESULTS CONCLUSIONS AND IMPLICATIONS

Background and PurposeDevelopment of combination therapies has received significant interest in recent years. Previously, a two‐receptor one‐transducer (2R‐1T) model was proposed to characterize drug interactions with two receptors that lead to the same phenotypic response through a common transducer pathway. We applied, for the first time, the 2R‐1T model to characterize the interaction of noradrenaline and arginine‐vasopressin on vasoconstriction and performed inter‐species scaling to humans using this mechanism‐based model. Experimental ApproachContractile data were obtained from in vitro rat small mesenteric arteries after exposure to single or combined challenges of noradrenaline and arginine‐vasopressin with or without pretreatment with the irreversible α‐adrenoceptor antagonist, phenoxybenzamine. Data were analysed using the 2R‐1T model to characterize the observed exposure–response relationships and drug–drug interaction. The model was then scaled to humans by accounting for differences in receptor density. Key ResultsWith receptor affinities set to published values, the 2R‐1T model satisfactorily characterized the interaction between noradrenaline and arginine‐vasopressin in rat small mesenteric arteries (relative standard error ≤20%), as well as the effect of phenoxybenzamine. Furthermore, after scaling the model to human vascular tissue, the model also adequately predicted the interaction between both agents on human renal arteries. Conclusions and ImplicationsThe 2R‐1T model can be of relevance to quantitatively characterize the interaction between two drugs that interact via different receptors and a common transducer pathway. Its mechanistic properties are valuable for scaling the model across species. This approach is therefore of significant value to rationally optimize novel combination treatments.

Drug–target binding kinetics (as determined by association and dissociation rate constants, kon and koff) can be an important determinant of the kinetics of drug action. However, the effect compartment model is used most frequently instead of a target binding model to describe hysteresis. Here we investigate when the drug–target binding model should be used in lieu of the effect compartment model. The utility of the effect compartment (EC), the target binding kinetics (TB) and the combined effect compartment–target binding kinetics (EC–TB) model were tested on either plasma (ECPL, TBPL and EC–TBPL) or brain extracellular fluid (ECF) (ECECF, TBECF and EC–TBECF) morphine concentrations and EEG amplitude in rats. It was also analyzed when a significant shift in the time to maximal target occupancy (TmaxTO) with increasing dose, the discriminating feature between the TB and EC model, occurs in the TB model. All TB models assumed a linear relationship between target occupancy and drug effect on the EEG amplitude. All three model types performed similarly in describing the morphine pharmacodynamics data, although the EC model provided the best statistical result. The analysis of the shift in TmaxTO (∆TmaxTO) as a result of increasing dose revealed that ∆TmaxTO is decreasing towards zero if the koff is much smaller than the elimination rate constant or if the target concentration is larger than the initial morphine concentration. The results for the morphine PKPD modelling and the analysis of ∆TmaxTO indicate that the EC and TB models do not necessarily lead to different drug effect versus time curves for different doses if a delay between drug concentrations and drug effect (hysteresis) is described. Drawing mechanistic conclusions from successfully fitting one of these two models should therefore be avoided. Since the TB model can be informed by in vitro measurements of kon and koff, a target binding model should be considered more often for mechanistic modelling purposes. Electronic supplementary material The online version of this article (10.1007/s10928-018-9593-x) contains supplementary material, which is available to authorized users.

In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.

For scaling drug plasma clearance (CLp) from adults to children, extrapolations of population pharmacokinetic (PopPK) covariate models between drugs sharing an elimination pathway have enabled accelerated development of pediatric models and dosing recommendations. This study aims at identifying conditions for which this approach consistently leads to accurate pathway specific CLp scaling from adults to children for drugs undergoing hepatic metabolism. A physiologically based pharmacokinetic (PBPK) simulation workflow utilizing mechanistic equations defining hepatic metabolism was developed. We found that drugs eliminated via the same pathway require similar pediatric dose adjustments only in specific cases, depending on drugs extraction ratio, unbound fraction, type of binding plasma protein, and the fraction metabolized by the isoenzyme pathway for which CLp is scaled. Overall, between-drug extrapolation of pediatric covariate functions for CLp is mostly applicable to low and intermediate extraction ratio drugs eliminated by one isoenzyme and binding to human serum albumin in children older than 1 month.

Background and Aims: Replacement FIX therapy (rIX) is an effective treatment for hemophilia B even with undetectable levels in the blood 1. However, the mechanistic reason for hemostasis with low plasma levels is not well understood. There is growing evidence that FIX interactions with one or multiple binding partners (BP), may play a significant role in the exposure and hemostatic efficacy of rIX 2,3. The aim of this study is to explore this hypothesis by comparing the plasma PK, tissue biodistribution, and in vivo endpoints of different rIX variants using a mouse QSP model. Method: in vitro and in vivo FIX-KO mice studies and mathematical models were used to build a QSP model consisting of 8 tissue compartments , with each tissue divided into vascular, endothelial and interstitial spaces 4,5. The model simulates endogenous mouse IgG (mIgG), mouse serum albumin (MSA), and rIX dynamics including key clearance and distribution mechanisms. Competition for the endothelial FcRn receptor between Fc, albumin, mIgG, and MSA is explicitly modeled 6,7,8. The model was calibrated using mouse studies of radiolabeled rIX-Fc (Alprolix®), rIX-WT (BeneFIX®), and rIX-FP (Idelvion®). Tail-clip experiments following administration of rIX-Fc, rIX-WT, and rIX-FP were used to correlate the predicted exposures with the observed effects on bleeding time and total blood loss. Results: Preliminary simulations proved that having at least one BP best explains the rapid distribution of rIX-Fc and rIX-WT into the tissues, and the long plasma T 1/2 of rIX-Fc and rIX-FP. Visual predictive checks of the full PBPK model showed good agreement with the PK in the tissues. The best fit was achieved using a specific arrangement of four distinct binding partners: Shared BP (SBP) between all compounds (e.g. N-terminal binder) located within the vasculature with estimated K D of 470/600/4100 nM, for rIX-WT/rIX-FP/rIX-Fc, respectively. BP binding specific to rIX-WT (e.g. C-terminal binder) located in the interstitium of the tissue (varying densities) with estimated K D of 23 nM BP binding only for rIX-FP (e.g. albumin binder) located in both; the vasculature and interstitium of the tissue with estimated K D 20/0.05 μM (vascular/interstitial) BP binding only for rIX-Fc (e.g. Fc binder) located in the interstitium of tissue (varying densities) with estimated K D 3 μM The high degree of extravasation of rIX-Fc (and rIX-WT to a lesser degree) results in rapid distribution and sequestration in the tissues. The limited extravasation of rIX-FP and its high affinity to the SBP, results in increased recovery and a greater pool of bound rIX available in the tissue vasculature. Additionally, strong inverse correlation between the bound rIX in the vasculature and bleeding time/total blood loss suggests that the vascular pool plays a more significant role in FIX pharmacology, as compared to the pool in the extravascular space. Conclusion: The mouse QSP model demonstrated that the plasma and tissue biodistribution of rIX-Fc, rIX-FP, and rIX-WT cannot be explained without a BP, and that it is plausible to assume that different binding partners, both intra- and extravascular, for different rFIX variants exist. The correlation between the levels of bound rIX and the coagulation endpoints suggests that the vascular bound rIX may be the pharmacologically active pool or reservoir for haemostasis. The extravasation and sequestration of rIX-WT and rIX-Fc into the tissues may explain the decreased vascular exposure, and hence, the reduced efficacy (increased bleeding time/total blood loss) at later time points. Although the exact identity of the BP's remains to be further elucidated, the model estimates of their affinity, density and location provide guidance for further experimental investigations. Expansion of the QSP model with additional data and coagulation kinetics will further our understanding of the role of BPs in rIX pharmacology. References 1Srivastava A et al (2013) Haemophilia 19(1), e1-47 2Feng D et al (2013) JTH, Vol. 11 (12), 2176-2178 3Cheung WF et al (1996) PNAS USA, 93(20), 11068-11073 4Li L et al (2014) AAPS Journal 16(5), 1097-1109 5Shah DK & Betts AM (2012) J Pharmacokinet Pharmacodyn 39(1), 67-86 6Chia J et al (2018) J Biol Chem 293(17), 6363-6373 7Andersen JT et al (2010) J Biol Chem 285(7), 4826-4836 8Andersen JT et al (2013) J Biol Chem 288(33), 24277-24285 Disclosures Pestel: CSL Behring Innovation GmbH: Current Employment, Current equity holder in publicly-traded company. Rezvani-Sharif: CSL Behring Ltd: Current Employment, Current equity holder in publicly-traded company. Muir: CSL Behring Ltd: Current Employment, Current holder of stock options in a privately-held company. Krupa: CSL Behring LLC: Current Employment, Current equity holder in publicly-traded company. Brechmann: CSL Behring Innovation GmbH, Ended employment in the past 24 months: Bayer Ag (Bayer Pharmaceuticals),: Current Employment, Ended employment in the past 24 months, Patents & Royalties: Bayer. Verhagen: CSL Behring Ltd: Current Employment, Current equity holder in publicly-traded company. Dower: CSL Behring Ltd: Current Employment, Current equity holder in publicly-traded company. Herzog: CSL Behring GmbH: Current Employment, Current equity holder in publicly-traded company.

PF-05105679 is a moderately potent TRPM8 blocker which has been evaluated for the treatment of cold pain sensitivity. The TRPM8 channel is responsible for the sensation of cold environmental temperatures and has been implicated in regulation of core body temperature. Consequently, blockade of TRPM8 has been suggested to result in lowering of core body temperature. As part of the progression to human studies, the effect of PF-05105679 on core body temperature has been investigated in animals. Safety pharmacology studies showed that PF-05105679 reduced core body temperature in a manner that was inversely related to body weight of the species tested (greater exposure to PF-05105679 was required to lower temperature by 1°C in higher species). Based on an allometric (body weight) relationship, it was hypothesized that PF-05105679 would not lower core body temperature in humans at exposures that could exhibit pharmacological effects on cold pain sensation. On administration to humans, PF-05105679 was indeed effective at reversing the cold pain sensation associated with the cold pressor test in the absence of effects on core body temperature.

A modeling and simulation approach was used for quantitative comparison of a new generation HER2 antibody drug conjugate (ADC, PF-06804103) with trastuzumab-DM1 (T-DM1). To compare preclinical efficacy, the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of PF-06804103 and T-DM1 was determined across a range of mouse tumor xenograft models, using a tumor growth inhibition model. The tumor static concentration was assigned as the minimal efficacious concentration. PF-06804103 was concluded to be more potent than T-DM1 across cell lines studied. TSCs ranged from 1.0 to 9.8 µg/mL (n = 7) for PF-06804103 and from 4.7 to 29 µg/mL (n = 5) for T-DM1. Two experimental models which were resistant to T-DM1, responded to PF-06804103 treatment. A mechanism-based target mediated drug disposition (TMDD) model was used to predict the human PK of PF-06804103. This model was constructed and validated based on T-DM1 which has non-linear PK at doses administered in the clinic, driven by binding to shed HER2. Non-linear PK is predicted for PF-06804103 in the clinic and is dependent upon circulating HER2 extracellular domain (ECD) concentrations. The models were translated to human and suggested greater efficacy for PF-06804103 compared to T-DM1. In conclusion, a fit-for-purpose translational PK/PD strategy for ADCs is presented and used to compare a new generation HER2 ADC with T-DM1. Electronic supplementary material The online version of this article (10.1007/s10928-020-09702-3) contains supplementary material, which is available to authorized users.

MID3: Mission Impossible, or Model‐Informed, Drug Discovery and Development? At the 2019 American Society for Clinical Pharmacology and Therapeutics (ASCPT) annual meeting, point‐counterpoint discussions were held on key challenges that limit, and future directions that enhance the adoption of model‐informed drug discovery and development (MID3) across the drug discovery, development, regulatory, and utilization continuum. We envision that the opportunities discussed and lessons learned from having contrasting perspectives on issues that lack consensus may aid our discipline in more effectively implementing MID3 principles.

The quantification of the effect of pharmacological treatment on the cardiovascular system is complicated because of the high level of interindividual and circadian variability. Recently, a dopamine-somatostatin chimera, BIM23B065, was under investigation to concurrently target the somatostatin and dopamine D2 receptors for the treatment of neuroendocrine tumors. However, both dopamine and somatostatin interact with different components of the cardiovascular system. This study established the response of the heart rate and the systolic blood pressure after administration of BIM23B065 in healthy male volunteers by analysis of the rate-pressure product (RPP), in a model-informed analysis. The RPP in the supine position of placebo-treated subjects showed a clear circadian component, best described by 2 cosine functions. The pharmacokinetics of BIM23B065 and its metabolite were best described using 2-compartment models with different forms of elimination kinetics. The administration of BIM23B065 gave a statistically significant reduction in the RPP, after which the effect diminished because of the tolerance to the cardiovascular effects after prolonged exposure to BIM23B065. This model provided insight in the circadian rhythm of the RPP in the supine position and the level of interindividual variability in healthy male volunteers. The developed population pharmacokinetic/pharmacodynamic model quantified the interaction between BIM23B065 and the RPP, informing on the clinical pharmacological properties of BIM23B065.

Aims Several clinical trials have confirmed the therapeutic benefit of imipenem for treatment of lung infections. There is however no knowledge of the penetration of imipenem into the lung epithelial lining fluid (ELF), the site of action relevant for lung infections. Furthermore, although the plasma pharmacokinetics (PK) of imipenem has been widely studied, most studies have been based on selected patient groups. The aim of this analysis was to characterize imipenem plasma PK across populations and to quantify imipenem ELF penetration. Methods A population model for imipenem plasma PK was developed using data obtained from healthy volunteers, elderly subjects and subjects with renal impairment, in order to identify predictors for inter-individual variability (IIV) of imipenem PK. Subsequently, a clinical study which measured plasma and ELF concentrations of imipenem was included in order to quantify lung penetration. Results A two compartmental model best described the plasma PK of imipenem. Creatinine clearance and body weight were included as subject characteristics predictive for IIV on clearance. Typical estimates for clearance, central and peripheral volume, and inter-compartmental clearance were 11.5 l h–1, 9.37 l, 6.41 l, 13.7 l h–1, respectively (relative standard error (RSE)

A key challenge in the development of central nervous system drugs is the availability of drug target specific blood‐based biomarkers. As a new approach, we applied cluster‐based pharmacokinetic/pharmacodynamic (PK/PD) analysis in brain extracellular fluid (brainECF) and plasma simultaneously after 0, 0.17, and 0.86 mg/kg of the dopamine D2/3 agonist quinpirole (QP) in rats. We measured 76 biogenic amines in plasma and brainECF after single and 8‐day administration, to be analyzed by cluster‐based PK/PD analysis. Multiple concentration‐effect relations were observed with potencies ranging from 0.001–383 nM. Many biomarker responses seem to distribute over the blood‐brain barrier (BBB). Effects were observed for dopamine and glutamate signaling in brainECF, and branched‐chain amino acid metabolism and immune signaling in plasma. Altogether, we showed for the first time how cluster‐based PK/PD could describe a systems‐response across plasma and brain, thereby identifying potential blood‐based biomarkers. This concept is envisioned to provide an important connection between drug discovery and early drug development.