Mechanism-Based Pharmacokinetic-Pharmacodynamic Modeling: Biophase Distribution, Receptor Theory, and Dynamical Systems Analysis

Advances in mechanobiological pharmacokinetic-pharmacodynamic models of osteoporosis treatment - Pathways to optimise and exploit existing therapies

Osteoporosis (OP) is a chronic progressive bone disease which is characterised by reduction of bone matrix volume and changes in the bone matrix properties which can ultimately lead to bone fracture. The two major forms of OP are related to aging and/or menopause. With the worldwide increase of the elderly population, particularly age-related OP poses a serious health issue which puts large pressure on health care systems. A major challenge for development of new drug treatments for OP and comparison of drug efficacy with existing treatments is due to current regulatory requirements which demand testing of drugs based on bone mineral density (BMD) in phase 2 trials and fracture risk in phase 3 trials. This requires large clinical trials to be conducted and to be run for long time periods, which is very costly. This, together with the fact that there are already many drugs available for treatment of OP, makes the development of new drugs inhibitive. Furthermore, an increased trend of the use of different sequential drug therapies has been observed in OP management, such as sequential anabolic-anticatabolic drug treatment or switching from one anticatabolic drug to another. Running clinical trials for concurrent and sequential therapies is neither feasible nor practical due to large number of combinatorial possibilities. In silico mechanobiological pharmacokinetic-pharmacodynamic (PK-PD) models of OP treatments allow predictions beyond BMD, i.e. bone microdamage and degree of mineralisation can also be monitored. This will help to inform clinical drug usage and development by identifying the most promising scenarios to be tested clinically (confirmatory trials rather than exploratory only trials), optimise trial design and identify subgroups of the population that show benefit-risk profiles (both good and bad) that are different from the average patient. In this review, we provide examples of the predictive capabilities of mechanobiological PK-PD models. These include simulation results of PMO treatment with denosumab, implications of denosumab drug holidays and coupling of bone remodelling models with calcium and phosphate systems models that allows to investigate the effects of co-morbidities such as hyperparathyroidism and chronic kidney disease together with calcium and vitamin D status on drug efficacy.

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

Enhanced sensitivity of extracellular antibiotic resistance genes (ARGs) to environmental concentrations of antibiotic

As emerging contaminants, antibiotics are frequently present in various environments, particularly rivers, albeit often at sublethal concentrations (ng/L∼μg/L). Assessing the risk associated with these low levels, which are far below the lethal threshold for most organisms, remains challenging. In this study, using microcosms containing planktonic bacteria and biofilm, we examined how antibiotic resistance genes (ARGs) in different physical states, including intracellular ARGs (iARGs) and extracellular ARGs (eARGs) responded to these low-level antibiotics. Our findings reveal a positive correlation between sub-lethal antibiotic exposure (ranging from 0.1 to 10 μg/L) and increased prevalence (measured as ARG copies/16s rDNA) of both iARGs and eARGs in planktonic bacteria. Notably, eARGs demonstrated greater sensitivity to antibiotic exposure compared to iARGs, with a lower threshold (0.1 μg/L for eARGs versus 1 μg/L for iARGs) for abundance increase. Moreover, ARGs in biofilms demonstrates higher sensitivity to antibiotic exposure compared to planktonic bacteria. To elucidate the underlying mechanisms, we established an integrated population dynamics-pharmacokinetics-pharmacodynamics (PD-PP) model. This model indicates that the enhanced sensitivity of eARGs is primarily driven by an increased potential for plasmid release from cells under low antibiotic concentrations. Furthermore, the accumulation of antibiotic in biofilms induces a greater sensitivity of ARG compared to the planktonic bacteria. This study provides a fresh perspective on the development of antibiotic resistance and offers an innovative approach for assessing the risk of sublethal antibiotic in the environment.

Interspecies Brain PBPK Modeling Platform to Predict Passive Transport through the Blood-Brain Barrier and Assess Target Site Disposition

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood-brain barrier (BBB) permeability evaluation tools are needed to explore drugs' ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs.

Target Reserve and Turnover Parameters Determine Rightward Shift of Enalaprilat Potency From its Binding Affinity to the Angiotensin Converting Enzyme

Drug effects are often assumed to be directly proportional to the fraction of occupied targets. However, for a number of antagonists that exhibit target-mediated drug disposition (TMDD), such as angiotensin-converting enzyme (ACE) inhibitors, drug binding to the target at low concentrations may be significant enough to influence pharmacokinetics but insufficient to elicit a drug response (i.e., differences in drug-target binding affinity and potency). In this study, a pharmacokinetic/pharmacodynamic model for enalaprilat was developed in humans to provide a theoretical framework for assessing the relationship between ex vivo drug potency (IC50) and in vivo target-binding affinity (KD). The model includes competitive binding of angiotensin I and enalaprilat to ACE and accounts for the circulating target pool. Data were obtained from the literature, and model fitting and parameter estimation were conducted using maximum likelihood in ADAPT5. The model adequately characterized time-courses of enalaprilat concentrations and four biomarkers in the renin-angiotensin system and provided estimates for in vivo KD (0.646 nM) and system-specific parameters, such as total target density (32.0 nM) and fraction of circulating target (19.8%), which were in agreement with previous reports. Model simulations were used to predict the concentration-effect curve of enalaprilat, revealing a 6.3-fold increase in IC50 from KD. Additional simulations demonstrated that target reserve and degradation parameters are key factors determining the extent of shift of enalaprilat ex vivo potency from its in vivo binding affinity. This may be a common phenomenon for drugs exhibiting TMDD and has implications for translating binding affinity to potency in drug development.

A mechanistic assessment of the nature of pharmacodynamic drug-drug interaction in vivo and in vitro

Combination pharmacotherapy is becoming increasingly necessary because most diseases are pathophysiologically controlled at the subcellular level by target proteins in a combinatorial manner. We demonstrate the application of the stimulus-response mechanistic model in characterising the drug and physiological properties of pharmacodynamic drug-drug interactions (PDDI) using previously published in vitro and in vivo drug combination experiments. The in vitro experiment tested the effect of a combination of SCH66336 and 4-HPR on the survival of in squamous cell carcinoma cell lines, while the in vivo experiment tested the effect of a combination of cetuximab and cisplatin on tumour growth inhibition in female xenograft mice. The model adequately described both experiments, quantified both system and drug properties and predicted the nature of the PDDI mechanism. Strong baseline signals of 7.35 and 610 units existed in the in vitro and in vivo experiments respectively. An overall synergistic relationship (interaction index = 1.03E-8) was detected in the in vitro experiment. In the in vivo model, the overall interaction index was 70,139.45 implying an antagonistic interaction between the cisplatin and the cetuximab signals.

Translational predictions of phase 2a first-in-patient efficacy studies for antituberculosis drugs

Background

Phase 2a trials in tuberculosis typically use early bactericidal activity (EBA), the decline in sputum colony forming units (CFU) over 14 days, as the primary outcome for testing the efficacy of drugs as monotherapy. However, the cost of phase 2a trials can range from 7 to 19.6 million dollars on average, while more than 30% of drugs fail to progress to phase 3. Better utilizing preclinical data to predict and prioritize the most likely drugs to succeed will thus help accelerate drug development and reduce costs. We aim to predict clinical EBA using preclinical in vivo pharmacokinetic-pharmacodynamic (PKPD) data and a model-based translational pharmacology approach.

Methods and Findings

First, mouse PK, PD and clinical PK models were compiled. Second, mouse PKPD models were built to derive an exposure response relationship. Third, translational prediction of clinical EBA studies was performed using mouse PKPD relationships and informed by clinical PK models and species-specific protein binding. Presence or absence of clinical efficacy was accurately predicted from the mouse model. Predicted daily decreases of CFU in the first 2 days of treatment and between day 2 and day 14 were consistent with clinical observations.

Conclusion

This platform provides an innovative solution to inform or even replace phase 2a EBA trials, to bridge the gap between mouse efficacy studies and phase 2b and phase 3 trials, and to substantially accelerate drug development.

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

Response surface model comparison and combinations for remifentanil and propofol in describing response to esophageal instrumentation and adverse respiratory events

BACKGROUND

The primary aim of this essay was to explore the best fitting model in remifentanil-propofol combined administrations during esophageal instrumentation (EI) from five distinct response surface models. The secondary aim was to combine the models to give appropriate effect-site drug concentrations (Ces) range with maximal comfort and safety.

METHODS

The Greco, reduced Greco, Minto, Scaled C50 Hierarchy and Fixed C50 Hierarchy models were constructed to fit four drug effects: loss of response to esophageal instrumentation (LREI), loss of response to esophageal instrumentation revised (LREIR), intolerable ventilatory depression (IVD) and respiratory compromise (RC). Models were tested by chi-square statistical test and evaluated with Akaike Information Criterion (AIC). Model prediction performance were measured by successful prediction rate (SPR) and three prediction errors.

RESULTS

The reduced Greco model was the best fitting model for LREI and RC, and the Minto model was the best fitting model for LREIR and IVD. The SPRs of reduced Greco model for LREI and RC were 81.76% and 79.81%. The SPRs of Minto model for LREIR and IVD were 80.32% and 80.12%. Overlay of the reduced Greco model for LREI and Minto model for IVD offered visual aid for guidance in drug administration.

CONCLUSIONS

Using proper response surface model to fit different drug effects will describe the interactions between anesthetic drugs better. Combining response surface models to select the more reliable effect-site drug concentrations range can be used to guide clinical drug administration with greater safety and provide an improvement of anesthesia precision.

A novel cardiovascular systems model to quantify drugs effects on the inter‐relationship between contractility and other hemodynamic variables

The use of systems‐based pharmacological modeling approaches to characterize mode‐of‐action and concentration‐effect relationships for drugs on specific hemodynamic variables has been demonstrated. Here, we (i) expand a previously developed hemodynamic system model through integration of cardiac output (CO) with contractility (CTR) using pressure‐volume loop theory, and (ii) evaluate the contribution of CO data for identification of system‐specific parameters, using atenolol as proof‐of‐concept drug. Previously collected experimental data was used to develop the systems model, and included measurements for heart rate (HR), CO, mean arterial pressure (MAP), and CTR after administration of atenolol (0.3–30 mg/kg) from three in vivo telemetry studies in conscious Beagle dogs. The developed cardiovascular (CVS)‐contractility systems model adequately described the effect of atenolol on HR, CO, dP/dtmax, and MAP dynamics and allowed identification of both system‐ and drug‐specific parameters with good precision. Model parameters were structurally identifiable, and the true mode of action can be identified properly. Omission of CO data did not lead to a significant change in parameter estimates compared to a model that included CO data. The newly developed CVS‐contractility systems model characterizes short‐term drug effects on CTR, CO, and other hemodynamic variables in an integrated and quantitative manner. When the baseline value of total peripheral resistance is predefined, CO data was not required to identify drug‐ and system‐specific parameters. Confirmation of the consistency of system‐specific parameters via inclusion of data for additional drugs and species is warranted. Ultimately, the developed model has the potential to be of relevance to support translational CVS safety studies.

Precision Cardio-Oncology: Use of Mechanistic Pharmacokinetic and Pharmacodynamic Modeling to Predict Cardiotoxicities of Anti-Cancer Drugs

The cardiotoxicity of anti-cancer drugs presents as a challenge to both clinicians and patients. Significant advances in cancer treatments have improved patient survival rates, but have also led to the chronic effects of anti-cancer therapies becoming more prominent. Additionally, it is difficult to clinically predict the occurrence of cardiovascular toxicities given that they can be transient or irreversible, with large between-subject variabilities. Further, cardiotoxicities present a range of different symptoms and pathophysiological mechanisms. These notwithstanding, mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling offers an important approach to predict cardiotoxicities and offering precise cardio-oncological care. Efforts have been made to integrate the structures of physiological and pharmacological networks into PK-PD modeling to the end of predicting cardiotoxicities based on clinical evaluation as well as individual variabilities, such as protein expression, and physiological changes under different disease states. Thus, this review aims to report recent progress in the use of PK-PD modeling to predict cardiovascular toxicities, as well as its application in anti-cancer therapies.

Pediatric Drug-Drug Interaction Evaluation: Drug, Patient Population, and Methodological Considerations

Hospitalized pediatric patients and those with complex or chronic conditions treated on an outpatient basis are commonly prescribed multiple drugs, resulting in increased risk for drug-drug interactions (DDIs). Although dedicated DDI evaluations are routinely performed in healthy adult volunteers during drug development, they are rarely performed in pediatric patients because of ethical, logistical, and methodological challenges. In the absence of pediatric DDI evaluations, adult DDI data are often extrapolated to pediatric patients. However, the magnitude of a DDI in pediatric patients may differ from adults because of age-dependent physiological changes that can impact drug disposition or response and because of other factors related to the drug (eg, dose, formulation) and the patient population (eg, disease state, obesity). Therefore, the DDI magnitude needs to be assessed in children separately from adults, although a lack of clinical DDI data in pediatric populations makes this evaluation challenging. As a result, pediatric DDI assessment relies on the predictive performance of the pharmacometric approaches used, such as population and physiologically based pharmacokinetic modeling. Therefore, careful consideration needs to be given to adequately account for the age-dependent physiological changes in these models to build high confidence for such untested DDI scenarios. This review article summarizes the key considerations related to the drug, patient population, and methodology, and how they can impact DDI evaluation in the pediatric population.

Suaeda vermiculata Aqueous-Ethanolic Extract-Based Mitigation of CCl4-Induced Hepatotoxicity in Rats, and HepG-2 and HepG-2/ADR Cell-Lines-Based Cytotoxicity Evaluations

Suaeda vermiculata, an edible halophytic plant, used by desert nomads to treat jaundice, was investigated for its hepatoprotective bioactivity and safety profile on its mother liquor aqueous-ethanolic extract. Upon LC-MS (Liquid Chromatography-Mass Spectrometry) analysis, the presence of several constituents including three major flavonoids, namely quercetin, quercetin-3-O-rutinoside, and kaempferol-O-(acetyl)-hexoside-pentoside were confirmed. The aqueous-ethanolic extract, rich in antioxidants, quenched the DPPH (1,1-diphenyl-2-picrylhydrazyl) radicals, and also showed noticeable levels of radical scavenging capacity in ABTS (2,2'-azino-bis-3-ethylbenzthiazoline-6-sulphonic acid) assay. For the hepatoprotective activity confirmation, the male rat groups were fed daily, for 7 days (n = 8/group, p.o.), either carboxyl methylcellulose (CMC) 0.5%, silymarin 200 mg/kg, the aqueous-ethanolic extract of the plant Suaeda vermiculata (100, 250, and 500 mg/kg extract), or quercetin (100 mg/kg) alone, and on day 7 of the administrations, all the animal groups, excluding a naïve (250 mg/kg aqueous-ethanolic extract-fed), and an intact animal group were induced hepatotoxicity by intraperitoneally administering carbon tetrachloride (CCl₄). All the animals were sacrificed after 24 h, and aspartate transaminase and alanine transaminase serum levels were observed, which were noted to be significantly decreased for the aqueous-ethanolic extract, silymarin, and quercetin-fed groups in comparison to the CMC-fed group (p < 0.0001). No noticeable adverse effects were observed on the liver, kidney, or heart's functions of the naïve (250 mg/kg) group. The aqueous-ethanolic extract was found to be safe in the acute toxicity (5 g/kg) test and showed hepatoprotection and safety at higher doses. Further upon, the cytotoxicity testings in HepG-2 and HepG-2/ADR (Adriamycin resistant) cell-lines were also investigated, and the IC₅₀ values were recorded at 56.19±2.55 µg/mL, and 78.40±0.32 µg/mL (p < 0.001, Relative Resistance RR 1.39), respectively, while the doxorubicin (Adriamycin) IC₅₀ values were found to be 1.3±0.064, and 4.77±1.05 µg/mL (p < 0.001, RR 3.67), respectively. The HepG-2/ADR cell-lines when tested in a combination of the aqueous-ethanolic extract with doxorubicin, a significant reversal in the doxorubicin's IC₅₀ value by 2.77 folds (p < 0.001, CI = 0.56) was noted as compared to the cytotoxicity test where the extract was absent. The mode of action for the reversal was determined to be synergistic in nature indicating the role of the aqueous-ethanolic extract.

Mechanism-based modeling of the effect of a novel inhibitor of vascular adhesion protein-1 on albuminuria and renal function markers in patients with diabetic kidney disease

The vascular adhesion protein-1 (VAP-1) inhibitor ASP8232 reduces albuminuria in patients with type 2 diabetes and chronic kidney disease. A mechanism-based model was developed to quantify the effects of ASP8232 on renal markers from a placebo-controlled Phase 2 study in diabetic kidney disease with 12 weeks of ASP8232 treatment. The model incorporated the available pharmacokinetic, pharmacodynamic (plasma VAP-1 concentration and activity), serum and urine creatinine, serum cystatin C, albumin excretion rate, urinary albumin-to-creatinine ratio, and urine volume information in an integrated manner. Drug-independent time-varying changes and different drug effects could be quantified for these markers using the model. Through simulations, this model provided the opportunity to dissect the relationship and longitudinal association between the estimated glomerular filtration rate and albuminuria and to quantify the pharmacological effects of ASP8232. The developed drug-independent model may be useful as a starting point for other compounds affecting the same biomarkers in a similar time scale.

Development of New Tuberculosis Drugs: Translation to Regimen Composition for Drug-Sensitive and Multidrug-Resistant Tuberculosis

Tuberculosis (TB) kills more people than any other infectious disease. Challenges for developing better treatments include the complex pathology due to within-host immune dynamics, interpatient variability in disease severity and drug pharmacokinetics-pharmacodynamics (PK-PD), and the growing emergence of resistance. Model-informed drug development using quantitative and translational pharmacology has become increasingly recognized as a method capable of drug prioritization and regimen optimization to efficiently progress compounds through TB drug development phases. In this review, we examine translational models and tools, including plasma PK scaling, site-of-disease lesion PK, host-immune and bacteria interplay, combination PK-PD models of multidrug regimens, resistance formation, and integration of data across nonclinical and clinical phases.We propose a workflow that integrates these tools with computational platforms to identify drug combinations that have the potential to accelerate sterilization, reduce relapse rates, and limit the emergence of resistance.

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.

Recent advances in long-acting nanoformulations for delivery of antiretroviral drugs

In spite of introduction of combination antiretroviral therapy (cART) against human immunodeficiency virus (HIV) infection; inaccessibility and poor adherence to oral cART costs 10 in 100,000 death worldwide. Failure in adherence leads to viral rebound, emergence of drug resistance and anticipated HIV infection in high risk individuals. Various Long-acting antiretroviral (LA ARV) nanoformulations including nano-prodrug, solid drug nanoparticles (SDN), nanocrystals, aspherical nanoparticles, polymeric and lipidic nanoparticles have shown plasma/tissue drug concentration in the therapeutic range for several weeks during pre-clinical evaluation. LA ARV nanoformulations therefore have replaced cART as better alternative for the treatment of HIV infection. Cabenuva™ is recently approved by Health Canada containing LA cabotegravir+LA rilpivirine nanocrystals (ViiV healthcare) for once monthly administration by intramuscular route. The LA nanoformulation due to its nanosize insist on better stability, delivery to lymphatic, slow release into systemic circulation via lymphatic-circulatory system conjoint and secondary drug depot within infiltered immune cells at site of administration and systemic circulation in contrast to conventional drugs. However, the pharmacokinetic, biodistribution and efficacy of LA nanoformulations hinge onto physicochemical properties of the drugs and route of administration. Therefore, current review emphasizes on these contradistinctive factors that affects the reproducibility, safety, efficacy and toxicity of LA anti-HIV nanoformulations. Moreover, it expatiates on application of profuse nanoformulations for long-acting effect with promising preclinical discoveries and two clinical leads. To add on, utilization of physiology-based and mechanism-based pharmacokinetic modelling and in vivo animal models which could lead to enhanced safety and efficacy of LA ARV nanoformulations in humans have been included.

100 years of modelling ligand-receptor binding and response: A focus on GPCRs

Experimental pharmacologists rely on the application of models to describe biological observations in order to learn about a drug's effective concentration, the strength with which it binds its target and drives a response (at either molecular or system level), and the nature of more complex drug actions (allosterism/functional selectivity). Models in current use build upon decades of basic principles, going back to the beginning of the last century. Yet often, researchers are only partially familiar with these underlying principles, creating the potential for confusion due to failure to recognise the underpinning assumptions of the models that are used. Here, we describe the history of receptor theory as it underpins receptor stimulus-response models in use today, emphasising particularly attributes and models relevant to GPCRs-and point to some current aims of model development.

Analytical considerations for postmortem metabolomics using GC-high-resolution MS

Metabolomics studies that aim to qualitatively and quantitatively characterize the entirety of small endogenous biomolecules in an organism are widely conducted in the clinical setting. They also become more and more popular in the field of forensics (toxicology), e.g., to assist in postmortem investigations by objective postmortem interval estimation. However, other issues in postmortem toxicology, such as the phenomenon of (time-dependent) postmortem redistribution, have not yet been tackled by metabolomics studies. Hence, the aim of the current study was to develop an (un)targeted gas chromatography-high-resolution mass spectrometry-based method for endogenous metabolites as a tool for large-scale (un)targeted human postmortem metabolomics investigations (e.g., to objectively assess PMR) with thorough analytical evaluation of this method to ensure fitness-to-purpose in terms of reliability and robustness. This was achieved by using a targeted metabolite subset (n = 56) and a targeted processing workflow. Evaluation experiments have shown that using an artificial matrix (revised simulated body fluid (rSBF) + 5% bovine serum albumin (BSA)) for calibration purposes, all parameters lay within the scope of the method (sensitivity, selectivity, calibration model, accuracy, precision, processed sample stability, and extraction efficiency). When applying this method to large-scale studies, samples should be run in randomized order if analysis time is expected to exceed 18-24 h and potential biomarkers that are found with this method should be verified by a specialized, targeted method (e.g., by using standard addition in authentic matrix for quantification purposes). Overall, the current method can be successfully used for conduction of time-dependent postmortem metabolomics investigations. Graphical abstract.

Why is the therapeutic effect of acute antimigraine drugs delayed? A review of controlled trials and hypotheses about the delay of effect

In randomised controlled trials (RCTs) of oral drug treatment of migraine attacks, efficacy is evaluated after 2 hours. The effect of oral naratriptan 2.5 mg with a maximum blood concentration (Tmax ) at 2 hours increases from 2 to 4 hours in RCTs. To check whether such a delayed effect is also present for other oral antimigraine drugs, we hand-searched the literature for publications on RCTs reporting efficacy. Two triptans, 3 nonsteroidal anti-inflammatory drugs (NSAIDs), a triptan combined with an NSAID and a calcitonin gene-related peptide receptor antagonist were evaluated for their therapeutic gain with determination of time to maximum effect (Emax ). Emax was compared with known Tmax from pharmacokinetic studies to estimate the delay to pain-free. The delay in therapeutic gain varied from 1-2 hours for zolmitriptan 5 mg to 7 hours for naproxen 500 mg. An increase in effect from 2 to 4 hours was observed after eletriptan 40 mg, frovatriptan 2.5 mg and lasmiditan 200 mg, and after rizatriptan 10 mg (Tmax  = 1 h) from 1 to 2 hours. This strongly indicates a general delay of effect in oral antimigraine drugs. A review of 5 possible effects of triptans on the trigemino-vascular system did not yield a simple explanation for the delay. In addition, Emax for triptans probably depends partly on the rise in plasma levels and not only on its maximum. The most likely explanation for the delay in effect is that a complex antimigraine system with more than 1 site of action is involved.

Computational model of the dual action of PTH - Application to a rat model of osteoporosis

This paper presents a pharmacokinetic/pharmacodynamic (PK/PD) model of the action of PTH(1-34) on bone modelling and remodelling, developed for quantitatively investigating the dose- and administration pattern-dependency of the bone tissue response to this drug. Firstly, a PK model of PTH(1-34) was developed, accounting for administration via subcutaneous injections. Subsequently, the PK model was coupled to a (mechanistic) bone cell population model of bone modelling and remodelling, taking into account the effects of PTH(1-34) on the differentiation of lining cells into active osteoblasts, on the apoptosis of active osteoblasts, and on proliferation of osteoblast precursors, as well as on the key regulatory pathways of bone cell activities. Numerical simulations show that the coupled PK/PD model is able to distinguish between continuous and intermittent administration patterns of PTH(1-34), in terms of yielding both catabolic bone responses (if drug administration is carried out continuously) and anabolic bone responses (if drug administration is carried out intermittently). The model also features a non-linear relation between bone gain and drug dose (as known from experiments); doubling the dose from 80 μg/kg/day to 160 μg/kg/day induced a 1.3-fold increase of the bone volume-to-total volume ratio. Furthermore, the model presented in this paper confirmed that bone modelling represents an essential mechanism of the anabolic response of bone to PTH(1-34) administration in rat models, and that the large amount of bone formation observed in such models cannot be explained via remodelling alone.

Stem cell bioengineering: building from stem cell biology

New fundamental discoveries in stem cell biology have yielded potentially transformative regenerative therapeutics. However, widespread implementation of stem-cell-derived therapeutics remains sporadic. Barriers that impede the development of these therapeutics can be linked to our incomplete understanding of how the regulatory networks that encode stem cell fate govern the development of the complex tissues and organs that are ultimately required for restorative function. Bioengineering tools, strategies and design principles represent core components of the stem cell bioengineering toolbox. Applied to the different layers of complexity present in stem-cell-derived systems - from gene regulatory networks in single stem cells to the systemic interactions of stem-cell-derived organs and tissues - stem cell bioengineering can address existing challenges and advance regenerative medicine and cellular therapies.

Dissecting the Pharmacodynamics and Pharmacokinetics of MSCs to Overcome Limitations in Their Clinical Translation

Recently, mesenchymal stromal stem cells (MSCs) have been proposed as therapeutic agents because of their promising preclinical features and good safety profile. However, their introduction into clinical practice has been associated with a suboptimal therapeutic profile. In this review, we address the biodistribution of MSCs in preclinical studies with a focus on the current understanding of the pharmacodynamics (PD) and pharmacokinetics (PK) of MSCs as key aspects to overcome unsatisfactory clinical benefits of MSC application. Beginning with evidence of MSC biodistribution and highlighting PK and PD factors, a new PK-PD model is also proposed. According to this theory, MSCs and their released factors are key players in PK, and the efficacy biomarkers are considered relevant for PD in more predictive preclinical investigations. Accounting for the PK-PD relationship in MSC translational research and proposing new models combined with better biodistribution studies could allow realization of the promise of more robust MSC clinical translation.

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.

Opioid-induced respiratory depression in humans: a review of pharmacokinetic-pharmacodynamic modelling of reversal

BACKGROUND

Opioids are potent painkillers but come with serious adverse effects ranging from addiction to potentially lethal respiratory depression. A variety of drugs with separate mechanisms of action are available to prevent or reverse opioid-induced respiratory depression (OIRD).

METHODS

The authors reviewed human studies on reversal of OIRD using models that describe and predict the time course of pharmacokinetics (PK) and pharmacodynamics (PD) of opioids and reversal agents and link PK to PD.

RESULTS

The PKPD models differ in their basic structure to capture the specific pharmacological mechanisms by which reversal agents interact with opioid effects on breathing. The effect of naloxone, a competitive opioid receptor antagonist, is described by the combined effect-compartment receptor-binding model to quantify rate limitation at the level of drug distribution and receptor kinetics. The effects of reversal agents that act through non-opioidergic pathways, such as ketamine and the experimental drug GAL021, are described by physiological models, in which stimulants act at CO₂ chemosensitivity, CO₂-independent ventilation, or both. The PKPD analyses show that although all reversal strategies may be effective under certain circumstances, there are conditions at which reversal is less efficacious and sometimes even impossible.

CONCLUSIONS

Model-based drug development is needed to design an 'ideal' reversal agent-that is, one that is not influenced by opioid receptor kinetics, does not interfere with opioid analgesia, has a rapid onset of action with long-lasting effects, and is devoid of adverse effects.

Time-Dependent Postmortem Redistribution of Opioids in Blood and Alternative Matrices

Forensic postmortem case interpretation can be challenging, in particular due to postmortem redistribution (PMR) phenomena. Recent studies have shown that computed tomography (CT)-guided collection of biopsy samples using a robotic arm (virtobot) provides a valuable tool for systematic studies on time-dependent PMR. Utilizing this strategy, several cases involving opioid use such as methadone, fentanyl, tramadol, codeine, oxycodone and hydrocodone were evaluated for time-dependent concentration changes and potential redistribution mechanisms. Upon admission to the institute (t1), blood (femoral and right ventricle heart blood) and tissue biopsy samples (lung, kidney, liver, spleen, thigh muscle and adipose tissue) were collected utilizing CT-guided biopsy. Approximately 24 h later (t2; mean 28 ± 15 h), during the autopsy, samples from the same body regions were collected manually and in addition brain tissue, gastric content, urine and left ventricle heart blood. Analysis was conducted with liquid chromatography tandem mass spectrometry. Significant time-dependent methadone concentration increases in femoral blood (pB) indicate the occurrence of PMR, however, ultimately not relevant for forensic interpretation. The main metabolite of methadone, 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP), showed a less significant trend for PMR. Redistribution by passive diffusion along the muscle-to-pB concentration gradient seems likely for methadone, but not for EDDP. Results for fentanyl suggest extensive PMR. Other opioids such as tramadol, codeine, hydrocodone and oxycodone showed no consistent trend for significant PMR. Overall, CT-guided biopsy sampling proved to be a valuable tool for the investigation of PMR mechanisms.

Impact of soft protein interactions on the excretion, extent of receptor occupancy and tumor accumulation of ultrasmall metal nanoparticles: a compartmental model simulation

Ultrasmall metal nanoparticles (NPs) are next-generation nano-based platforms for in vivo disease diagnosis and treatment. Due to their small size below the kidney filtration threshold and marked resistance to nonspecific serum protein adsorption, ultrasmall NPs can be rapidly excreted through the kidneys and escape liver uptake. However, although ultrasmall particles may be deemed highly resistant to protein adsorption, the real extent of this resistance is not known. Here, a simple compartmental model simulation was therefore implemented to understand how NP behavior in vivo could be modulated by soft, transient NP–plasma protein interactions characterized by dissociation constants in the millimolar range. In Model 1, ultrasmall NPs functionalized with a targeting probe, plasma proteins and target receptors were assumed to co-exist within a single compartment. Simulations were performed to understand the synergistic effect of soft interactions, systemic clearance and NP size on receptor occupancy in the single compartment. The results revealed the existence of a narrow range of ultraweak affinities and optimal particle sizes leading to greater target occupancy. In Model 2, simulations were performed to understand the impact of soft interactions on NP accumulation into a peripheral (tumor) compartment. The results revealed that soft interactions – but not active targeting – enhanced tumor uptake levels when tumor accumulation was limited by ‘fast’ plasma clearance and ‘slow’ vascular extravasation. The simple model presented here provides a basic framework to quantitatively understand the blood and tumor pharmacokinetics of ultrasmall NPs under the influence of transient protein interactions.

A compartmental model simulation shows that the blood and tumor pharmacokinetics of ultrasmall metal nanoparticles can be modulated by soft interactions with plasma proteins.

Optimization of maintenance therapy of Risperidone with CYP2D6 genetic polymorphisms through an extended translational framework-based prediction of target occupancies/clinical outcomes

Risperidone, one of the second-generation antipsychotics, can efficiently target dopamine D₂ and serotonin 5-HT_2A receptors. There actually exists significant implication of CYP2D6 genetic polymorphisms on the metabolic kinetics of risperidone, little is known about the extent of CYP2D6 impacting human D2 and 5-HT2A receptor occupancies as well as the clinical efficacy and efficacy in schizophrenia treatment. Here we assessed the influences of CYP2D6 gene polymorphisms on human target occupancies/clinical outcomes and optimized the maintenance therapy of risperidone. A translational framework, previously developed using in vitro and in vivo information in rats, was used as the basis for integrating the effects of CYP2D6 genetic polymorphisms on target occupancies and clinical outcomes. D₂ occupancy as a biomarker was related to Positive and Negative Syndrome Scale (PANSS) response and Simpson-Angus Scale (SAS). The population approach was applied to characterize pharmacokinetic and pharmacodynamic (PK/PD) profiles of risperidone. Non-compartment analysis method was performed to calculate the steady state PK/PD parameters of both risperidone and 9-hydroxyrisperidone. The predictive power of this extended translational framework was determined by comparing the predictions of target occupancies and clinical outcomes with the reported human values of risperidone at clinically suggested dosage of 4.0 mg/day. This extended translational framework was adequately used to predict human target occupancies and clinical outcomes. At the steady state, D₂ ROs were 75.8%, 79.3% and 86.0% for CYP2D6 poor metabolizer (PM), intermediate metabolizer (IM) and extensive metabolizer (EM), respectively; 5-HT_2A ROs were 96.4%, 97.2% and 98.4% for CYP2D6 PM, IM and EM, respectively; PANSS changes from placebo were -5.3, -7.7 and -11.3 for CYP2D6 PM, IM and EM, respectively; SAS changes from placebo were 0.13, 0.15 and 0.18 for CYP2D6 PM, IM and EM, respectively. The predictions of human D₂, 5-HT_2A RO, PANSS and SAS changes for risperidone with CYP2D6 genetic polymorphisms were well in line with the reported values in clinic. 5.0, 4.0 and 2.5 mg/day were the equivalent dosages of risperidone for CYP2D6 PM, IM and EM, respectively. The optimized maintenance therapy of risperidone was provided through the Three-Step method and the dosage range was 2.5-5.0 mg/day for three CYP2D6 gene groups in the present study. Taken together, our findings demonstrate that this extended translational framework not only differentiates the effects of CYP2D6 genetic polymorphisms on target occupancies and clinical outcomes, but also constitutes a scientific basis to optimize the maintenance therapy of neuropsychiatric patients in clinic.

Fingerprints of CNS drug effects: a plasma neuroendocrine reflection of D2 receptor activation using multi-biomarker pharmacokinetic/pharmacodynamic modelling

BACKGROUND AND PURPOSE

Because biological systems behave as networks, multi-biomarker approaches increasingly replace single biomarker approaches in drug development. To improve the mechanistic insights into CNS drug effects, a plasma neuroendocrine fingerprint was identified using multi-biomarker pharmacokinetic/pharmacodynamic (PK/PD) modelling. Short- and long-term D2 receptor activation was evaluated using quinpirole as a paradigm compound.

EXPERIMENTAL APPROACH

Rats received 0, 0.17 or 0.86 mg·kg-1 of the D2 agonist quinpirole i.v. Quinpirole concentrations in plasma and brain extracellular fluid (brainECF ), 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.

KEY RESULTS

The quinpirole pharmacokinetics were adequately described by a two-compartment model with an unbound brainECF -to-plasma concentration ratio of 5. The release of adenocorticotropic hormone (ACTH), growth hormone, prolactin and thyroid-stimulating hormone (TSH) from the pituitary was influenced. Except for ACTH, D2 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.

CONCLUSIONS AND IMPLICATIONS

The integrated multi-biomarker PK/PD approach revealed a fingerprint reflecting D2 receptor activation. This forms the conceptual basis for in vivo evaluation of on- and off-target CNS drug effects. The effect of treatment duration is highly relevant given the long-term use of D2 agonists in clinical practice. Further development towards quantitative systems pharmacology models will eventually facilitate mechanistic drug development.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

Quantitative systems pharmacology analysis of drug combination and scaling to humans: the interaction between noradrenaline and vasopressin in vasoconstriction

BACKGROUND AND PURPOSE

Development 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 APPROACH

Contractile 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 RESULTS

With 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 IMPLICATIONS

The 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.

Quantitative Translational Analysis of Brain Kynurenic Acid Modulation via Irreversible Kynurenine Aminotransferase II Inhibition

Kynurenic acid (KYNA) plays a significant role in maintaining normal brain function, and abnormalities in KYNA levels have been associated with various central nervous system disorders. Confirmation of its causality in human diseases requires safe and effective modulation of central KYNA levels in the clinic. The kynurenine aminotransferases (KAT) II enzyme represents an attractive target for pharmacologic modulation of central KYNA levels; however, KAT II and KYNA turnover kinetics, which could contribute to the duration of pharmacologic effect, have not been reported. In this study, the kinetics of central KYNA-lowering effect in rats and nonhuman primates (NHPs, Cynomolgus macaques) was investigated using multiple KAT II irreversible inhibitors as pharmacologic probes. Mechanistic pharmacokinetic-pharmacodynamic analysis of in vivo responses to irreversible inhibition quantitatively revealed that 1) KAT II turnover is relatively slow [16-76 hours' half-life (t_1/2)], whereas KYNA is cleared more rapidly from the brain (<1 hour t_1/2) in both rats and NHPs, 2) KAT II turnover is slower in NHPs than in rats (76 hours vs. 16 hours t_1/2, respectively), and 3) the percent contribution of KAT II to KYNA formation is constant (∼80%) across rats and NHPs. Additionally, modeling results enabled establishment of in vitro-in vivo correlation for both enzyme turnover rates and drug potencies. In summary, quantitative translational analysis confirmed the feasibility of central KYNA modulation in humans. Model-based analysis, where system-specific properties and drug-specific properties are mechanistically separated from in vivo responses, enabled quantitative understanding of the KAT II-KYNA pathway, as well as assisted development of promising candidates to test KYNA hypothesis in humans.

Development and Translational Application of a Minimal Physiologically Based Pharmacokinetic Model for a Monoclonal Antibody against Interleukin 23 (IL-23) in IL-23-Induced Psoriasis-Like Mice

The interleukin (IL)-23/T_h17/IL-17 immune pathway has been identified to play an important role in the pathogenesis of psoriasis. Many therapeutic proteins targeting IL-23 or IL-17 are currently under development for the treatment of psoriasis. In the present study, a mechanistic pharmacokinetics (PK)/pharmacodynamics (PD) study was conducted to assess the target-binding and disposition kinetics of a monoclonal antibody (mAb), CNTO 3723, and its soluble target, mouse IL-23, in an IL-23-induced psoriasis-like mouse model. A minimal physiologically based pharmacokinetic model with target-mediated drug disposition features was developed to quantitatively assess the kinetics and interrelationship between CNTO 3723 and exogenously administered, recombinant mouse IL-23 in both serum and lesional skin site. Furthermore, translational applications of the developed model were evaluated with incorporation of human PK for ustekinumab, an anti-human IL-23/IL-12 mAb developed for treatment of psoriasis, and human disease pathophysiology information in psoriatic patients. The results agreed well with the observed clinical data for ustekinumab. Our work provides an example on how mechanism-based PK/PD modeling can be applied during early drug discovery and how preclinical data can be used for human efficacious dose projection and guide decision making during early clinical development of therapeutic proteins.

Sobol Sensitivity Analysis: A Tool to Guide the Development and Evaluation of Systems Pharmacology Models

A systems pharmacology model typically integrates pharmacokinetic, biochemical network, and systems biology concepts into a unifying approach. It typically consists of a large number of parameters and reaction species that are interlinked based upon the underlying (patho)physiology and the mechanism of drug action. The more complex these models are, the greater the challenge of reliably identifying and estimating respective model parameters. Global sensitivity analysis provides an innovative tool that can meet this challenge. CPT Pharmacometrics Syst. Pharmacol. (2015) 4, 69-79; doi:10.1002/psp4.6; published online 25 February 2015.

On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology

The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.

Interstitial IgG antibody pharmacokinetics assessed by combined in vivo- and physiologically-based pharmacokinetic modelling approaches

KEY POINTS

For therapeutic antibodies, total tissue concentrations are frequently reported as a lump sum measure of the antibody in residual plasma, interstitial fluid and cells. In terms of correlating antibody exposure to a therapeutic effect, however, interstitial pharmacokinetics might be more relevant. In the present study, we collected total tissue and interstitial antibody biodistribution data in mice and assessed the composition of tissue samples aiming to correct total tissue measurements for plasma and cellular content. All data and parameters were integrated into a refined physiologically-based pharmacokinetic model for monoclonal antibodies to enable the tissue-specific description of antibody pharmacokinetics in the interstitial space. We found that antibody interstitial concentrations are highly tissue-specific and dependent on the underlying capillary structure but, in several tissues, they reach relatively high interstitial concentrations, contradicting the still-prevailing view that both the distribution to tissues and the interstitial concentrations for antibodies are generally low.

ABSTRACT

For most therapeutic antibodies, the interstitium is the target space. Although experimental methods for measuring antibody pharmacokinetics (PK) in this space are not well established, thus making quantitative assessment difficult, the interstitial antibody concentration is assumed to be low. In the present study, we combined direct quantification of antibodies in the interstitial fluid with a physiologically-based PK (PBPK) modelling approach, with the aim of better describing the PK of monoclonal antibodies in the interstitial space of different tissues. We isolated interstitial fluid by tissue centrifugation and conducted an antibody biodistribution study in mice, measuring total tissue and interstitial concentrations in selected tissues. Residual plasma, interstitial volumes and lymph flows, which are important PBPK model parameters, were assessed in vivo. We could thereby refine the PBPK modelling of monoclonal antibodies, better interpret antibody biodistribution data and more accurately predict their PK in the different tissue spaces. Our results indicate that, in tissues with discontinuous capillaries (liver and spleen), interstitial concentrations are reflected by the plasma concentration. In tissues with continuous capillaries (e.g. skin and muscle), ∼50-60% of the plasma concentration is found in the interstitial space. In the brain and kidney, on the other hand, antibodies are restricted to the vascular space. Our data may significantly impact the interpretation of biodistribution data of monoclonal antibodies and might be important when relating measured concentrations to a therapeutic effect. By contrast to the view that the antibody distribution to the interstitial space is limited, using direct measurements and model-based data interpretation, we show that high antibody interstitial concentrations are reached in most tissues.

From receptor binding kinetics to signal transduction; a missing link in predicting in vivo drug-action

An important question in drug discovery is how to overcome the significant challenge of high drug attrition rates due to lack of efficacy and safety. A missing link in the understanding of determinants for drug efficacy is the relation between drug-target binding kinetics and signal transduction, particularly in the physiological context of (multiple) endogenous ligands. We hypothesized that the kinetic binding parameters of both drug and endogenous ligand play a crucial role in determining cellular responses, using the NK1 receptor as a model system. We demonstrated that the binding kinetics of both antagonists (DFA and aprepitant) and endogenous agonists (NKA and SP) have significantly different effects on signal transduction profiles, i.e. potency values, in vitro efficacy values and onset rate of signal transduction. The antagonistic effects were most efficacious with slowly dissociating aprepitant and slowly associating NKA while the combination of rapidly dissociating DFA and rapidly associating SP had less significant effects on the signal transduction profiles. These results were consistent throughout different kinetic assays and cellular backgrounds. We conclude that knowledge of the relationship between in vitro drug-target binding kinetics and cellular responses is important to ultimately improve the understanding of drug efficacy in vivo.

Population Pharmacokinetic/Pharmacodynamic Modeling of Sunitinib by Dosing Schedule in Patients with Advanced Renal Cell Carcinoma or Gastrointestinal Stromal Tumor

Background

Sunitinib is a multi-targeted tyrosine kinase inhibitor used in the treatment of advanced renal cell carcinoma (RCC) and imatinib-resistant/intolerant gastrointestinal stromal tumors (GIST).

Methods

A meta-analysis of 10 prospective clinical studies in advanced RCC and GIST was performed to support the development of pharmacokinetic (PK) and PK/pharmacodynamic (PD) models that account for the effects of important covariates. These models were used to make predictions with respect to the PK, safety, and efficacy of sunitinib when administered on the traditional 4-weeks-on/2-weeks-off schedule (Schedule 4/2) versus an alternative schedule of 2 weeks on/1 week off (Schedule 2/1).

Results

The covariates found to have a significant effect on one or more of the PK or PD parameter studies included, age, sex, body weight, race, baseline Eastern Cooperative Oncology Group performance status, tumor type, and dosing schedule. The models predicted that, in both RCC and GIST patients, Schedule 2/1 would have comparable efficacy to Schedule 4/2, despite some differences in PK profiles. The models also predicted that, in both indications, sunitinib-related thrombocytopenia would be less severe when sunitinib was administered on Schedule 2/1 dosing compared with Schedule 4/2.

Conclusion

These findings support the use of sunitinib on Schedule 2/1 as a potential alternative to Schedule 4/2 because it allows for the management of toxicity without loss of efficacy.

Electronic supplementary material

The online version of this article (doi:10.1007/s40262-016-0404-5) contains supplementary material, which is available to authorized users.