The role of mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modelling in translational research of biologics

Machine-learning-based adverse drug event prediction from observational health data: A review

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions and fatalities. Machine learning models have been developed to assess the individual patient risk of having an ADE. In this article, we have reviewed studies addressing the prediction of ADEs in observational health data with machine learning. The field of individualised ADE prediction is rapidly emerging through the increasing availability of additional data modalities (e.g., genetic data, screening data, wearables data) and advanced deep learning models such as transformers. Consequently, personalised adverse drug event predictions are becoming more feasible and tangible.

A Quantitative Systems Pharmacology Model Describing the Cellular Kinetic-Pharmacodynamic Relationship for a Live Biotherapeutic Product to Support Microbiome Drug Development

Live biotherapeutic products (LBPs) are human microbiome therapies showing promise in the clinic for a range of diseases and conditions. Describing the kinetics and behavior of LBPs poses a unique modeling challenge because, unlike traditional therapies, LBPs can expand, contract, and colonize the host digestive tract. Here, we present a novel cellular kinetic-pharmacodynamic quantitative systems pharmacology model of an LBP. The model describes bacterial growth and competition, vancomycin effects, binding and unbinding to the epithelial surface, and production and clearance of butyrate as a therapeutic metabolite. The model is calibrated and validated to published data from healthy volunteers. Using the model, we simulate the impact of treatment dose, frequency, and duration as well as vancomycin pretreatment on butyrate production. This model enables model-informed drug development and can be used for future microbiome therapies to inform decision making around antibiotic pretreatment, dose selection, loading dose, and dosing duration.

Bioinformatics and Computer Simulation approaches to the discovery and analysis of Bioactive Peptides

The traditional process of separating and purifying bioactive peptides is laborious and time-consuming. Using a traditional process to identify is difficult, and there is a lack of fast and accurate activity evaluation methods. How to extract bioactive peptides quickly and efficiently is still the focus of bioactive peptides research. In order to improve the present situation of the research, bioinformatics techniques and peptidome methods are widely used in this field. At the same time, bioactive peptides have their own specific pharmacokinetic characteristics, so computer simulation methods have incomparable advantages in studying the pharmacokinetics and pharmacokinetic-pharmacodynamic correlation models of bioactive peptides. The purpose of this review is to summarize the combined applications of bioinformatics and computer simulation methods in the study of bioactive peptides, with focuses on the role of bioinformatics in simulating the selection of enzymatic hydrolysis and precursor proteins, activity prediction, molecular docking, physicochemical properties, and molecular dynamics. Our review shows that new bioactive peptide molecular sequences with high activity can be obtained by computer-aided design. The significance of the pharmacokinetic-pharmacodynamic correlation model in the study of bioactive peptides is emphasized. Finally, some problems and future development potential of bioactive peptides binding new technologies are prospected.

Pharmacodynamic measures within tumors expose differential activity of PD(L)-1 antibody therapeutics

Macromolecules such as monoclonal antibodies (mAbs) are likely to experience poor tumor penetration because of their large size, and thus low drug exposure of target cells within a tumor could contribute to suboptimal responses. Given the challenge of inadequate quantitative tools to assess mAb activity within tumors, we hypothesized that measurement of accessible target levels in tumors could elucidate the pharmacologic activity of a mAb and could be used to compare the activity of different mAbs. Using positron emission tomography (PET), we measured the pharmacodynamics of immune checkpoint protein programmed-death ligand 1 (PD-L1) to evaluate pharmacologic effects of mAbs targeting PD-L1 and its receptor programmed cell death protein 1 (PD-1). For PD-L1 quantification, we first developed a small peptide-based fluorine-18-labeled PET imaging agent, [¹⁸F]DK222, which provided high-contrast images in preclinical models. We then quantified accessible PD-L1 levels in the tumor bed during treatment with anti-PD-1 and anti-PD-L1 mAbs. Applying mixed-effects models to these data, we found subtle differences in the pharmacodynamic effects of two anti-PD-1 mAbs (nivolumab and pembrolizumab). In contrast, we observed starkly divergent target engagement with anti-PD-L1 mAbs (atezolizumab, avelumab, and durvalumab) that were administered at equivalent doses, correlating with differential effects on tumor growth. Thus, we show that measuring PD-L1 pharmacodynamics informs mechanistic understanding of therapeutic mAbs targeting PD-L1 and PD-1. These findings demonstrate the value of quantifying target pharmacodynamics to elucidate the pharmacologic activity of mAbs, independent of mAb biophysical properties and inclusive of all physiological variables, which are highly heterogeneous within and across tumors and patients.

Patient-Specific Organoid and Organ-on-a-Chip: 3D Cell-Culture Meets 3D Printing and Numerical Simulation

The last few decades have witnessed diversified in vitro models to recapitulate the architecture and function of living organs or tissues and contribute immensely to advances in life science. Two novel 3D cell culture models: 1) Organoid, promoted mainly by the developments of stem cell biology and 2) Organ-on-a-chip, enhanced primarily due to microfluidic technology, have emerged as two promising approaches to advance the understanding of basic biological principles and clinical treatments. This review describes the comparable distinct differences between these two models and provides more insights into their complementarity and integration to recognize their merits and limitations for applicable fields. The convergence of the two approaches to produce multi-organoid-on-a-chip or human organoid-on-a-chip is emerging as a new approach for building 3D models with higher physiological relevance. Furthermore, rapid advancements in 3D printing and numerical simulations, which facilitate the design, manufacture, and results-translation of 3D cell culture models, can also serve as novel tools to promote the development and propagation of organoid and organ-on-a-chip systems. Current technological challenges and limitations, as well as expert recommendations and future solutions to address the promising combinations by incorporating organoids, organ-on-a-chip, 3D printing, and numerical simulation, are also summarized.

Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions

With more than 90 approved drugs by 2020, therapeutic antibodies have played a central role in shifting the treatment landscape of many diseases, including autoimmune disorders and cancers. While showing many therapeutic advantages such as long half-life and highly selective actions, therapeutic antibodies still face many outstanding issues associated with their pharmacokinetics (PK) and pharmacodynamics (PD), including high variabilities, low tissue distributions, poorly-defined PK/PD characteristics for novel antibody formats, and high rates of treatment resistance. We have witnessed many successful cases applying PK/PD modeling to answer critical questions in therapeutic antibodies’ development and regulations. These models have yielded substantial insights into antibody PK/PD properties. This review summarized the progress, challenges, and future directions in modeling antibody PK/PD and highlighted the potential of applying mechanistic models addressing the development questions.

Quantitative modeling predicts competitive advantages of a next generation anti-NKG2A monoclonal antibody over monalizumab for the treatment of cancer

A semimechanistic pharmacokinetic (PK)/receptor occupancy (RO) model was constructed to differentiate a next generation anti-NKG2A monoclonal antibody (KSQ mAb) from monalizumab, an immune checkpoint inhibitor in multiple clinical trials for the treatment of solid tumors. A three-compartment model incorporating drug PK, biodistribution, and NKG2A receptor interactions was parameterized using monalizumab PK, in vitro affinity measurements for both monalizumab and KSQ mAb, and receptor burden estimates from the literature. Following calibration against monalizumab PK data in patients with rheumatoid arthritis, the model successfully predicted the published PK and RO observed in gynecological tumors and in patients with squamous cell carcinoma of the head and neck. Simulations predicted that the KSQ mAb requires a 10-fold lower dose than monalizumab to achieve a similar RO over a 3-week period following q3w intravenous (i.v.) infusion dosing. A global sensitivity analysis of the model indicated that the drug-target binding affinity greatly affects the tumor RO and that an optimal affinity is needed to balance RO with enhanced drug clearance due to target mediated drug disposition. The model predicted that the KSQ mAb can be dosed over a less frequent regimen or at lower dose levels than the current monalizumab clinical dosing regimen of 10 mg/kg q2w. Either dosing strategy represents a competitive advantage over the current therapy. The results of this study demonstrate a key role for mechanistic modeling in identifying optimal drug parameters to inform and accelerate progression of mAb to clinical trials.

Pharmacokinetic-Pharmacodynamic Modeling of Tumor Targeted Drug Delivery Using Nano-Engineered Mesenchymal Stem Cells

Nano-engineered mesenchymal stem cells (nano-MSCs) are promising targeted drug delivery platforms for treating solid tumors. MSCs engineered with paclitaxel (PTX) loaded poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) are efficacious in treating lung and ovarian tumors in mouse models. The quantitative description of pharmacokinetics (PK) and pharmacodynamics (PD) of nano-MSCs is crucial for optimizing their therapeutic efficacy and clinical translatability. However, successful translation of nano-MSCs is challenging due to their complex composition and physiological mechanisms regulating their pharmacokinetic-pharmacodynamic relationship (PK-PD). Therefore, in this study, a mechanism-based preclinical PK-PD model was developed to characterize the PK-PD relationship of nano-MSCs in orthotopic A549 human lung tumors in SCID Beige mice. The developed model leveraged literature information on diffusivity and permeability of PTX and PLGA NPs, PTX release from PLGA NPs, exocytosis of NPs from MSCs as well as PK and PD profiles of nano-MSCs from previous in vitro and in vivo studies. The developed PK-PD model closely captured the reported tumor growth in animals receiving no treatment, PTX solution, PTX-PLGA NPs and nano-MSCs. Model simulations suggest that increasing the dosage of nano-MSCs and/or reducing the rate of PTX-PLGA NPs exocytosis from MSCs could result in improved anti-tumor efficacy in preclinical settings.

Quantifying PD-L1 Expression to Monitor Immune Checkpoint Therapy: Opportunities and Challenges

Simple Summary

Malignant cells hijack the regulatory roles of immune checkpoint proteins for immune evasion and survival. Therapeutics blocking those proteins can restore the balance of the immune system and lead to durable responses in cancer patients. Although a subset of patients derive benefit, there are few non-invasive technologies to guide and monitor those therapies to improve success rates. This is a review of the advancements in non-invasive methods for quantification of immune checkpoint protein programmed death ligand 1 expression, a biomarker detected by immunohistochemistry and widely used for guiding immune checkpoint therapy.

Abstract

Therapeutics targeting programmed death ligand 1 (PD-L1) protein and its receptor PD-1 are now dominant players in restoring anti-tumor immune responses. PD-L1 detection by immunohistochemistry (IHC) is emerging as a reproducible biomarker for guiding patient stratification for those therapies in some cancers. However, PD-L1 expression in the tumor microenvironment is highly complex. It is upregulated by aberrant genetic alterations, and is highly regulated at the transcriptional, posttranscriptional, and protein levels. Thus, PD-L1 IHC is inadequate to fully understand the relevance of PD-L1 levels in the whole body and their dynamics to improve therapeutic outcomes. Imaging technologies could potentially assist in meeting that need. Early clinical investigations show promising results in quantifying PD-L1 expression in the whole body by positron emission tomography (PET). Within this context, this review summarizes advancements in regulation of PD-L1 expression and imaging agents, and in PD-L1 PET for drug development, and discusses opportunities and challenges presented by these innovations for guiding immune checkpoint therapy (ICT).

Use of translational modeling and simulation for quantitative comparison of PF-06804103, a new generation HER2 ADC, with Trastuzumab-DM1

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.

A novel semi-mechanistic tumor growth fraction model for translation of preclinical efficacy of anti-glypican 3 antibody drug conjugate to human

The growing fraction (GF) of tumor has been reported as one of the predictive markers of the efficacy of chemotherapeutics. Therefore, a semi-mechanistic model has been developed that describes tumor growth on the basis of cell cycle, allowing the incorporation of the GF of a tumor in pharmacokinetic/pharmacodynamic (PK/PD) modeling. Efficacy data of anti-glypican 3 (GPC3) antibody drug conjugate (ADC) in a hepatocellular carcinoma (HCC) patient derived xenograft (PDX) model was used for evaluation of this proposed model. Our model was able to describe the kinetics of growth inhibition of HCC PDX models following treatment with anti-GPC3 ADC remarkably well. The estimated tumurostatic concentrations were used in tandem with human PKs translated from cynomolgus monkey for prediction of the efficacious dose. The projected efficacious human dose of anti-GPC3 ADC was in the range 0.20-0.63 mg/kg for the Q3W dosing regimen, with a median dose of 0.50 mg/kg. This publication is the first step in evaluating the applicability of GF in PK/PD modeling of ADCs. The authors are hopeful that incorporation of GF will result in an improved translation of the preclinical efficacy of ADCs to clinical settings and thereby better prediction of the efficacious human dose.

PK/PD Mediated Dose Optimization of Emactuzumab, a CSF1R Inhibitor, in Patients With Advanced Solid Tumors and Diffuse-Type Tenosynovial Giant Cell Tumor

Targeted biological therapies may achieve maximal therapeutic efficacy at doses below the maximum tolerated dose (MTD); therefore, the search for the MTD in clinical studies may not be ideal for these agents. Emactuzumab is an investigational monoclonal antibody that binds to and inhibits the activation of the cell surface colony‐stimulating factor‐1 receptor. Here, we show how modeling target‐mediated drug disposition coupled with pharmacodynamic end points was used to optimize the dose of emactuzumab without defining an MTD. The model could be used to recommend doses across different disease indications. The approach recommended an optimal biological dose of emactuzumab for dosing every 2 weeks (q2w) ≥ 900 mg, approximately three‐fold lower than the highest dose tested clinically. The model predicted that emactuzumab doses ≥ 900 mg q2w would achieve target saturation in excess of 90% over the entire dosing cycle. Subsequently, a dose of 1,000 mg q2w was used in the extension phase of a phase I study of emactuzumab in patients with advanced solid tumors or diffuse‐type tenosynovial giant cell tumor. Clinical data from this study were consistent with model predictions. The model was also used to predict the optimum dose of emactuzumab for use with dosing every 3 weeks, enabling dosing flexibility with respect to comedications. In summary, this work demonstrates the value of quantitative clinical pharmacology approaches to dose selection in oncology as opposed to traditional MTD methods.

Mechanistic Quantitative Pharmacology Strategies for the Early Clinical Development of Bispecific Antibodies in Oncology

Bispecific antibodies (bsAbs) have become an integral component of the therapeutic research strategy to treat cancer. In addition to clinically validated immune cell re‐targeting, bsAbs are being designed for tumor targeting and as dual immune modulators. Explorative preclinical and emerging clinical data indicate potential for enhanced efficacy and reduced systemic toxicity. However, bsAbs are a complex modality with challenges to overcome in early clinical trials, including selection of relevant starting doses using a minimal anticipated biological effect level approach, and predicting efficacious dose despite nonintuitive dose response relationships. Multiple factors can contribute to variability in the clinic, including differences in functional affinity due to avidity, receptor expression, effector to target cell ratio, and presence of soluble target. Mechanistic modeling approaches are a powerful integrative tool to understand the complexities and aid in clinical translation, trial design, and prediction of regimens and strategies to reduce dose limiting toxicities of bsAbs. In this tutorial, the use of mechanistic modeling to impact decision making for bsAbs is presented and illustrated using case study examples.

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.

Characterization of concurrent target suppression by JNJ-61178104, a bispecific antibody against human tumor necrosis factor and interleukin-17A

Tumor necrosis factor (TNF) and interleukin (IL)-17A are pleiotropic cytokines implicated in the pathogenesis of several autoimmune diseases including rheumatoid arthritis (RA) and psoriatic arthritis (PsA). JNJ-61178104 is a novel human anti-TNF and anti-IL-17A monovalent, bispecific antibody that binds to both human TNF and human IL-17A with high affinities and blocks the binding of TNF and IL-17A to their receptors in vitro. JNJ-61178104 also potently neutralizes TNF and IL-17A-mediated downstream effects in multiple cell-based assays. In vivo, treatment with JNJ-61178104 resulted in dose-dependent inhibition of cellular influx in a human IL-17A/TNF-induced murine lung neutrophilia model and the inhibitory effects of JNJ-61178104 were more potent than the treatment with bivalent parental anti-TNF or anti-IL-17A antibodies. JNJ-61178104 was shown to engage its targets, TNF and IL-17A, in systemic circulation measured as drug/target complex formation in normal cynomolgus monkeys (cyno). Surprisingly, quantitative target engagement assessment suggested lower apparent in vivo target-binding affinities for JNJ-61178104 compared to its bivalent parental antibodies, despite their similar in vitro target-binding affinities. The target engagement profiles of JNJ-61178104 in humans were in general agreement with the predicted profiles based on cyno data, suggesting similar differences in the apparent in vivo target-binding affinities. These findings show that in vivo target engagement of monovalent bispecific antibody does not necessarily recapitulate that of the molar-equivalent dose of its bivalent parental antibody. Our results also offer valuable insights into the understanding of the pharmacokinetics/pharmacodynamics and target engagement of other bispecific biologics against dimeric and/or trimeric soluble targets in vivo.

Pharmacokinetic and pharmacodynamic insights from microfluidic intestine-on-a-chip models

Introduction: After administration, a drug undergoes absorption, distribution, metabolism, and elimination (ADME) before exerting its effect on the body. The combination of these process yields the pharmacokinetic (PK) and pharmacodynamic (PD) profiles of a drug. Although accurate prediction of PK and PD profiles is essential for drug development, conventional in vitro models are limited by their lack of physiological relevance. Recently, microtechnology-based in vitro model systems, termed 'organ-on-a-chip,' have emerged as a potential solution.Areas covered: Orally administered drugs are absorbed through the intestinal wall and transported to the liver before entering systemic circulation, which plays an important role in the PK and PD profiles. Recently developed, chip-based in vitro models can be useful models for simulating such processes and will be covered in this paper.Expert opinion: The potential of intestine-on-a-chip models combined with conventional PK-PD modeling has been demonstrated with promising preliminary results. However, there are several challenges to overcome. Development of the intestinal wall, integration of the gut microbiome, and the provision of an intestine-specific environment must be achieved to realize in vivo-like intestinal model and enhance the efficiency of drug development.

Determination of IL-23 Pharmacokinetics by Highly Sensitive Accelerator Mass Spectrometry and Subsequent Modeling to Project IL-23 Suppression in Psoriasis Patients Treated with Anti-IL-23 Antibodies

The pro-inflammatory cytokine interleukin (IL)-23 is a key modulator of the immune response, making it an attractive target for the treatment of autoimmune disease. Correspondingly, several monoclonal antibodies against IL-23 are either in development or approved for autoimmune indications such as psoriasis. Despite being a clinical validated target, IL-23 pharmacokinetics (e.g., IL-23 synthesis and elimination rates) and the degree of target suppression (i.e., decrease in free "active" IL-23) associated with clinical efficacy are not well understood, primarily due to its ultra-low circulating levels and the lack of sensitive and accurate measurement methods. In the current work, this issue was overcome by using accelerator mass spectrometry (AMS) to measure the concentration and pharmacokinetics of human recombinant [¹⁴C]-IL-23 following an intravenous trace-dose in cynomolgus monkeys. IL-23 pharmacokinetic parameters along with clinical drug exposure and IL-23 binding affinities from four different anti-IL-23 antibodies (ustekinumab, tildrakizumab, guselkumab, and risankizumab) were used to build a pharmacokinetics/pharmacodynamics (PK/PD) model to assess the time course of free IL-23 over one year in psoriasis patients following different dosing regimens. The predicted rank order of reduction of free IL-23 was consistent with their reported rank order of Psoriasis Area and Severity Index (PASI) 100 scores in clinical efficacy trials (ustekinumab < tildrakizumab < guselkumab < risankizumab), thus demonstrating the utility of highly sensitive AMS for determining target pharmacokinetics to inform PK/PD modeling and assessing target suppression associated with clinical efficacy.

Systematic Review of Host-Mediated Activity of Miltefosine in Leishmaniasis through Immunomodulation

Host immune responses are pivotal for the successful treatment of the leishmaniases, a spectrum of infections caused by Leishmania parasites. Previous studies speculated that augmenting cytokines associated with a type 1 T-helper cell (Th1) response is necessary to combat severe forms of leishmaniasis, and it has been hypothesized that the antileishmanial drug miltefosine is capable of immunomodulation and induction of Th1 cytokines.

Host immune responses are pivotal for the successful treatment of the leishmaniases, a spectrum of infections caused by Leishmania parasites. Previous studies speculated that augmenting cytokines associated with a type 1 T-helper cell (Th1) response is necessary to combat severe forms of leishmaniasis, and it has been hypothesized that the antileishmanial drug miltefosine is capable of immunomodulation and induction of Th1 cytokines. A better understanding of the immunomodulatory effects of miltefosine is central to providing a rationale regarding synergistic mechanisms of activity to combine miltefosine optimally with other conventional and future antileishmanials that are currently under development. Therefore, a systematic literature search was performed to evaluate to what extent and how miltefosine influences the host Th1 response. Miltefosine’s effects observed in both a preclinical and a clinical context associated with immunomodulation in the treatment of leishmaniasis are evaluated in this review. A total of 27 studies were included in the analysis. Based on the current evidence, miltefosine is not only capable of inducing direct parasite killing but also of modulating the host immunity. Our findings suggest that miltefosine-induced activation of Th1 cytokines, particularly represented by increased gamma interferon (IFN-γ) and interleukin 12 (IL-12), is essential to prevail over the Leishmania-driven Th2 response. Differences in miltefosine-induced host-mediated effects between in vitro, ex vivo, animal model, and human studies are further discussed. All things considered, an effective treatment with miltefosine is acquired by enhanced functional Th1 cytokine responses and may further be enhanced in combination with immunostimulatory agents.

Strategies for using mathematical modeling approaches to design and interpret multi-organ microphysiological systems (MPS)

Recent advances in organ-on-a-chip technology have resulted in numerous examples of microscale systems that faithfully mimic the physiology and pathology of human organs and diseases. The next step in this field, which has already been partially demonstrated at a proof-of-concept level, would be integration of organ modules to construct multiorgan microphysiological systems (MPSs). In particular, there is interest in "body-on-a-chip" models, which recapitulate complex and dynamic interactions between different organs. Integration of multiple organ modules, while faithfully reflecting human physiology in a quantitative sense, will require careful consideration of factors such as relative organ sizes, blood flow rates, cell numbers, and ratios of cell types. The use of a mathematical modeling platform will be an essential element in designing multiorgan MPSs and interpretation of experimental results. Also, extrapolation to in vivo will require robust mathematical modeling techniques. So far, several scaling methods and pharmacokinetic and physiologically based pharmacokinetic models have been applied to multiorgan MPSs, with each method being suitable to a subset of different objectives. Here, we summarize current mathematical methodologies used for the design and interpretation of multiorgan MPSs and suggest important considerations and approaches to allow multiorgan MPSs to recapitulate human physiology and disease progression better, as well as help in vitro to in vivo translation of studies on response to drugs or chemicals.

A Translational Quantitative Systems Pharmacology Model for CD3 Bispecific Molecules: Application to Quantify T Cell-Mediated Tumor Cell Killing by P-Cadherin LP DART®

CD3 bispecific antibody constructs recruit cytolytic T cells to kill tumor cells, offering a potent approach to treat cancer. T cell activation is driven by the formation of a trimolecular complex (trimer) between drugs, T cells, and tumor cells, mimicking an immune synapse. A translational quantitative systems pharmacology (QSP) model is proposed for CD3 bispecific molecules capable of predicting trimer concentration and linking it to tumor cell killing. The model was used to quantify the pharmacokinetic (PK)/pharmacodynamic (PD) relationship of a CD3 bispecific targeting P-cadherin (PF-06671008). It describes the disposition of PF-06671008 in the central compartment and tumor in mouse xenograft models, including binding to target and T cells in the tumor to form the trimer. The model incorporates T cell distribution to the tumor, proliferation, and contraction. PK/PD parameters were estimated for PF-06671008 and a tumor stasis concentration (TSC) was calculated as an estimate of minimum efficacious trimer concentration. TSC values ranged from 0.0092 to 0.064 pM across mouse tumor models. The model was translated to the clinic and used to predict the disposition of PF-06671008 in patients, including the impact of binding to soluble P-cadherin. The predicted terminal half-life of PF-06671008 in the clinic was approximately 1 day, and P-cadherin expression and number of T cells in the tumor were shown to be sensitive parameters impacting clinical efficacy. A translational QSP model is presented for CD3 bispecific molecules, which integrates in silico, in vitro and in vivo data in a mechanistic framework, to quantify and predict efficacy across species.

Multi-Organs-on-Chips: Towards Long-Term Biomedical Investigations

With advantageous features such as minimizing the cost, time, and sample size requirements, organ-on-a-chip (OOC) systems have garnered enormous interest from researchers for their ability for real-time monitoring of physical parameters by mimicking the in vivo microenvironment and the precise responses of xenobiotics, i.e., drug efficacy and toxicity over conventional two-dimensional (2D) and three-dimensional (3D) cell cultures, as well as animal models. Recent advancements of OOC systems have evidenced the fabrication of 'multi-organ-on-chip' (MOC) models, which connect separated organ chambers together to resemble an ideal pharmacokinetic and pharmacodynamic (PK-PD) model for monitoring the complex interactions between multiple organs and the resultant dynamic responses of multiple organs to pharmaceutical compounds. Numerous varieties of MOC systems have been proposed, mainly focusing on the construction of these multi-organ models, while there are only few studies on how to realize continual, automated, and stable testing, which still remains a significant challenge in the development process of MOCs. Herein, this review emphasizes the recent advancements in realizing long-term testing of MOCs to promote their capability for real-time monitoring of multi-organ interactions and chronic cellular reactions more accurately and steadily over the available chip models. Efforts in this field are still ongoing for better performance in the assessment of preclinical attributes for a new chemical entity. Further, we give a brief overview on the various biomedical applications of long-term testing in MOCs, including several proposed applications and their potential utilization in the future. Finally, we summarize with perspectives.

Using quantitative systems pharmacology to evaluate the drug efficacy of COX-2 and 5-LOX inhibitors in therapeutic situations

A quantitative analysis of dose-response relationships is essential in preclinical and clinical drug development in order to optimize drug efficacy and safety, respectively. However, there is a lack of quantitative understanding about the dynamics of pharmacological drug-target interactions in biological systems. In this study, a quantitative systems pharmacology (QSP) approach is applied to quantify the drug efficacy of cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitors by coupling physiologically based pharmacokinetic models, at the whole-body level, with affected biological networks, at the cellular scale. Both COX-2 and 5-LOX are key enzymes in the production of inflammatory mediators and are known targets in the design of anti-inflammatory drugs. Drug efficacy is here evaluated for single and appropriate co-treatment of diclofenac, celecoxib, zileuton, and licofelone by quantitatively studying the reduction of prostaglandins and leukotrienes. The impact of rifampicin pre-treatment on prostaglandin formation is also investigated by considering pharmacokinetic drug interactions with diclofenac and celecoxib, finally suggesting optimized dose levels to compensate for the reduced drug action. Furthermore, a strong correlation was found between pain relief observed in patients as well as celecoxib- and diclofenac-induced decrease in prostaglandins after 6 h. The findings presented reveal insights about drug-induced modulation of cellular networks in a whole-body context, thereby describing complex pharmacokinetic/pharmacodynamic behavior of COX-2 and 5-LOX inhibitors in therapeutic situations. The results demonstrate the clinical benefit of using QSP to predict drug efficacy and, hence, encourage its use in future drug discovery and development programs.

Time-dependent pharmacokinetics of dexamethasone and its efficacy in human breast cancer xenograft mice: a semi-mechanism-based pharmacokinetic/pharmacodynamic model

Dexamethasone (DEX) is the substrate of CYP3A. However, the activity of CYP3A could be induced by DEX when DEX was persistently administered, resulting in auto-induction and time-dependent pharmacokinetics (pharmacokinetics with time-dependent clearance) of DEX. In this study we investigated the pharmacokinetic profiles of DEX after single or multiple doses in human breast cancer xenograft nude mice and established a semi-mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model for characterizing the time-dependent PK of DEX as well as its anti-cancer effect. The mice were orally given a single or multiple doses (8 mg/kg) of DEX, and the plasma concentrations of DEX were assessed using LC-MS/MS. Tumor volumes were recorded daily. Based on the experimental data, a two-compartment model with first order absorption and time-dependent clearance was established, and the time-dependence of clearance was modeled by a sigmoid E_max equation. Moreover, a semi-mechanism-based PK/PD model was developed, in which the auto-induction effect of DEX on its metabolizing enzyme CYP3A was integrated and drug potency was described using an E_max equation. The PK/PD model was further used to predict the drug efficacy when the auto-induction effect was or was not considered, which further revealed the necessity of adding the auto-induction effect into the final PK/PD model. This study established a semi-mechanism-based PK/PD model for characterizing the time-dependent pharmacokinetics of DEX and its anti-cancer effect in breast cancer xenograft mice. The model may serve as a reference for DEX dose adjustments or optimization in future preclinical or clinical studies.

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.

A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim

Proteins are an increasingly important class of drugs used as therapeutic as well as diagnostic agents. A generic physiologically based pharmacokinetic (PBPK) model was developed in order to represent at whole body level the fundamental mechanisms driving the distribution and clearance of large molecules like therapeutic proteins. The model was built as an extension of the PK-Sim model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling as especially relevant for antibodies. For model development and evaluation, PK data was used for compounds with a wide range of solute radii. The model supports the integration of knowledge gained during all development phases of therapeutic proteins, enables translation from pre-clinical species to human and allows predictions of tissue concentration profiles which are of relevance for the analysis of on-target pharmacodynamic effects as well as off-target toxicity. The current implementation of the model replaces the generic protein PBPK model available in PK-Sim since version 4.2 and becomes part of the Open Systems Pharmacology Suite.

Pharmacokinetic/Pharmacodynamic Modeling for Drug Development in Oncology

High drug attrition rates remain a critical issue in oncology drug development. A series of steps during drug development must be addressed to better understand the pharmacokinetic (PK) and pharmacodynamic (PD) properties of novel agents and, thus, increase their probability of success. As available data continues to expand in both volume and complexity, comprehensive integration of PK and PD information into a robust mathematical model represents a very useful tool throughout all stages of drug development. During the discovery phase, PK/PD models can be used to identify and select the best drug candidates, which helps characterize the mechanism of action and disease behavior of a given drug, to predict clinical response in humans, and to facilitate a better understanding about the potential clinical relevance of preclinical efficacy data. During early drug development, PK/PD modeling can optimize the design of clinical trials, guide the dose and regimen that should be tested further, help evaluate proof of mechanism in humans, anticipate the effect in certain subpopulations, and better predict drug-drug interactions; all of these effects could lead to a more efficient drug development process. Because of certain peculiarities of immunotherapies, such as PK and PD characteristics, PK/PD modeling could be particularly relevant and thus have an important impact on decision making during the development of these agents.

Bridging Bio-Nano Science and Cancer Nanomedicine

The interface of bio-nano science and cancer medicine is an area experiencing much progress but also beset with controversy. Core concepts of the field-e.g., the enhanced permeability and retention (EPR) effect, tumor targeting and accumulation, and even the purpose of "nano" in cancer medicine-are hotly debated. In parallel, considerable advances in neighboring fields are occurring rapidly, including the recent progress of "immuno-oncology" and the fundamental impact it is having on our understanding and the clinical treatment of the group of diseases collectively known as cancer. Herein, we (i) revisit how cancer is commonly treated in the clinic and how this relates to nanomedicine; (ii) examine the ongoing debate on the relevance of the EPR effect and tumor targeting; (iii) highlight ways to improve the next-generation of nanomedicines; and (iv) discuss the emerging concept of working with (and not against) biology. While discussing these controversies, challenges, emerging concepts, and opportunities, we explore new directions for the field of cancer nanomedicine.

A Population Pharmacokinetic Model Incorporating Saturable Pharmacokinetics and Autoinduction for High Rifampicin Doses

Accumulating evidence suggests that increasing doses of rifampicin may shorten tuberculosis treatment. The PanACEA HIGHRIF1 trial assessed safety, pharmacokinetics, and antimycobacterial activity of rifampicin at doses up to 40 mg/kg. Eighty-three pulmonary tuberculosis patients received 10, 20, 25, 30, 35, or 40 mg/kg rifampicin daily over 2 weeks, supplemented with standard doses of isoniazid, pyrazinamide, and ethambutol in the second week. This study aimed at characterizing rifampicin pharmacokinetics observed in HIGHRIF1 using nonlinear mixed effects modeling. The final population pharmacokinetic model included an enzyme turnover model accounting for time-dependent elimination due to autoinduction, concentration-dependent clearance, and dose-dependent bioavailability. The relationship between clearance and concentration was characterized by a Michaelis-Menten relationship. The relationship between bioavailability and dose was described using an E_max relationship. The model will be key in determining exposure-response relationships for rifampicin and should be considered when designing future trials and when treating future patients with high-dose rifampicin.

Microtechnology-Based Multi-Organ Models

Drugs affect the human body through absorption, distribution, metabolism, and elimination (ADME) processes. Due to their importance, the ADME processes need to be studied to determine the efficacy and side effects of drugs. Various in vitro model systems have been developed and used to realize the ADME processes. However, conventional model systems have failed to simulate the ADME processes because they are different from in vivo, which has resulted in a high attrition rate of drugs and a decrease in the productivity of new drug development. Recently, a microtechnology-based in vitro system called "organ-on-a-chip" has been gaining attention, with more realistic cell behavior and physiological reactions, capable of better simulating the in vivo environment. Furthermore, multi-organ-on-a-chip models that can provide information on the interaction between the organs have been developed. The ultimate goal is the development of a "body-on-a-chip", which can act as a whole body model. In this review, we introduce and summarize the current progress in the development of multi-organ models as a foundation for the development of body-on-a-chip.

Towards Quantitative Systems Pharmacology Models of Chemotherapy-Induced Neutropenia

Neutropenia is a serious toxic complication of chemotherapeutic treatment. For years, mathematical models have been developed to better predict hematological outcomes during chemotherapy in both the traditional pharmaceutical sciences and mathematical biology disciplines. An increasing number of quantitative systems pharmacology (QSP) models that combine systems approaches, physiology, and pharmacokinetics/pharmacodynamics have been successfully developed. Here, I detail the shift towards QSP efforts, emphasizing the importance of incorporating systems-level physiological considerations in pharmacometrics.

Translational learning from clinical studies predicts drug pharmacokinetics across patient populations

Early indication of late-stage failure of novel candidate drugs could be facilitated by continuous integration, assessment, and transfer of knowledge acquired along pharmaceutical development programs. We here present a translational systems pharmacology workflow that combines drug cocktail probing in a specifically designed clinical study, physiologically based pharmacokinetic modeling, and Bayesian statistics to identify and transfer (patho-)physiological and drug-specific knowledge across distinct patient populations. Our work builds on two clinical investigations, one with 103 healthy volunteers and one with 79 diseased patients from which we systematically derived physiological information from pharmacokinetic data for a reference probe drug (midazolam) at the single-patient level. Taking into account the acquired knowledge describing (patho-)physiological alterations in the patient cohort allowed the successful prediction of the population pharmacokinetics of a second, candidate probe drug (torsemide) in the patient population. In addition, we identified significant relations of the acquired physiological processes to patient metadata from liver biopsies. The presented prototypical systems pharmacology approach is a proof of concept for model-based translation across different stages of pharmaceutical development programs. Applied consistently, it has the potential to systematically improve predictivity of pharmacokinetic simulations by incorporating the results of clinical trials and translating them to subsequent studies.

Physiologically based modeling together with Bayesian statistics allows the prediction of drug pharmacokinetics in specific patient populations. An interdisciplinary group of clinicians and computational scientists led by Dr. Lars Kuepfer from Bayer developed a generic workflow consisting of several consecutive learning steps where knowledge about both individual physiology as well as drug physicochemistry can be efficiently derived from plasma concentration profiles. The acquired information is then be used for the prediction of the pharmacokinetic behavior of a new drug candidate in a diseased population. This allows to simulate the variability in drug exposure virtually before starting clinical investigation in real patients in order to evaluate drug safety or efficacy through the simulation of virtual populations. Further development of this workflow could improve the safety of clinical development programs to assess the risk-benefit ratio of novel drug candidates in silico.

An allometric pharmacokinetic/pharmacodynamics model for BI 893923, a novel IGF-1 receptor inhibitor

PURPOSE

BI 893923 is a novel IGF1R/INSR inhibitor with promising anti-tumor efficacy. Dose-limiting hyperglycemia has been observed for other IGF1R/INSR inhibitors in clinical trials. To counterbalance anti-tumor efficacy with the risk of hyperglycemia and to determine the therapeutic window, we aimed to develop a translational pharmacokinetic/pharmacodynamics model for BI 893923. This aimed to translate pharmacokinetics and pharmacodynamics from animals to humans by an allometrically scaled semi-mechanistic model.

METHODS

Model development was based on a previously published PK/PD model for BI 893923 in mice (Titze et al., Cancer Chemother Pharmacol 77:1303-1314, 13). PK and blood glucose parameters were scaled by allometric principles using body weight as a scaling factor along with an estimation of the parameter exponents. Biomarker and tumor growth parameters were extrapolated from mouse to human using the body weight ratio as scaling factor.

RESULTS

The allometric PK/PD model successfully described BI 893923 pharmacokinetics and blood glucose across mouse, rat, dog, minipig, and monkey. BI 893923 human exposure as well as blood glucose and tumor growth were predicted and compared for different dosing scenarios. A comprehensive risk-benefit analysis was conducted by determining the net clinical benefit for each schedule. An oral dose of 2750 mg BI 893923 divided in three evenly distributed doses was identified as the optimal human dosing regimen, predicting a tumor growth inhibition of 90.4% without associated hyperglycemia.

CONCLUSION

Our model supported human therapeutic dose estimation by rationalizing the optimal efficacious dosing regimen with minimal undesired effects. This modeling approach may be useful for PK/PD scaling of other IGF1R/INSR inhibitors.

Translational Model-Based Strategy to Guide the Choice of Clinical Doses for Antibody-Drug Conjugates

This work proposes a model-based approach to help select the phase 1 dosing regimen for the antibody-drug conjugate (ADC) SAR408701 leveraging the available data for 2 other ADCs of the same construct: SAR3419 and SAR566658. First, monkey and human pharmacokinetic (PK) data of SAR566658 and SAR3419 were used to establish the appropriate allometric approach to be applied to SAR408701 monkey PK data. Second, a population pharmacokinetics-pharmacodynamics (PK-PD) model was developed to describe tumor volume evolution following SAR408701 injection in mice. Third, allometric approaches identified for SAR566658 and SAR3419 were applied to SAR408701 monkey PK data to predict the human PK profile. Both SAR566658 and SAR3419 human and monkey PK were best described by a 2-compartment linear model. The relative difference was less than 10% between predicted and observed clearance using allometric exponents of 0.75 and 1, respectively. Tumor volume evolution following SAR408701 injection was best described by a full Simeoni model with a plasma concentration threshold of 4.6 μg/mL for eradication in mice. Both allometric exponents were used to predict SAR408701 PK in human from PK in monkey and to identify the potential effective dosing regimens. This translational strategy may be a valuable tool to design future clinical studies for ADCs, to support selection of the most appropriate dosing regimen, and to estimate the minimal dose required to assure antitumor activity, according to the schedule used.

Optimal Affinity of a Monoclonal Antibody: Guiding Principles Using Mechanistic Modeling

Affinity optimization of monoclonal antibodies (mAbs) is essential for developing drug candidates with the highest likelihood of clinical success; however, a quantitative approach for setting affinity requirements is often lacking. In this study, we computationally analyzed the in vivo mAb-target binding kinetics to delineate general principles for defining optimal equilibrium dissociation constant ([Formula: see text]) of mAbs against soluble and membrane-bound targets. Our analysis shows that in general [Formula: see text] to achieve 90% coverage for a soluble target is one tenth of its baseline concentration ([Formula: see text]), and is independent of the dosing interval, target turnover rate or the presence of competing ligands. For membrane-bound internalizing targets, it is equal to the ratio of internalization rate of mAb-target complex and association rate constant ([Formula: see text]). In cases where soluble and membrane-bound forms of the target co-exist, [Formula: see text] lies within a range determined by the internalization rate ([Formula: see text]) of the mAb-membrane target complex and the ratio of baseline concentrations of soluble and membrane-bound forms ([Formula: see text]). Finally, to demonstrate practical application of these general rules, we collected target expression and turnover data to project [Formula: see text] for a number of marketed mAbs against soluble (TNFα, RANKL, and VEGF) and membrane-bound targets (CD20, EGFR, and HER2).

Determination of the starting dose in the first-in-human clinical trials with monoclonal antibodies: a systematic review of papers published between 1990 and 2013

A systematic review was performed to evaluate how the maximum recommended starting dose (MRSD) was determined in first-in-human (FIH) studies with monoclonal antibodies (mAbs). Factors associated with the choice of each MRSD determination method were also identified. PubMed was searched for FIH studies with mAbs published in English between January 1, 1990 and December 31, 2013, and the following information was extracted: MRSD determination method, publication year, therapeutic area, antibody type, safety factor, safety assessment results after the first dose, and number of dose escalation steps. Seventy-nine FIH studies with mAbs were identified, 49 of which clearly reported the MRSD determination method. The no observed adverse effects level (NOAEL)-based approach was the most frequently used method, whereas the model-based approach was the least commonly used method (34.7% vs 16.3%). The minimal anticipated biological effect level (MABEL)- or minimum effective dose (MED)-based approach was used more frequently in 2011–2013 than in 1990–2007 (31.6% vs 6.3%, P=0.036), reflecting a slow, but steady acceptance of the European Medicines Agency’s guidance on mitigating risks for FIH clinical trials (2007). The median safety factor was much lower for the MABEL- or MED-based approach than for the other MRSD determination methods (10 vs 32.2–53). The number of dose escalation steps was not significantly different among the different MRSD determination methods. The MABEL-based approach appears to be safer and as efficient as the other MRSD determination methods for achieving the objectives of FIH studies with mAbs faster.

A mathematical analysis of rebound in a target-mediated drug disposition model: II. With feedback

We consider the possibility of free receptor (antigen/cytokine) levels rebounding to higher than the baseline level after the application of an antibody drug using a target-mediated drug disposition model. It is assumed that the receptor synthesis rate experiences homeostatic feedback from the receptor levels. It is shown for a very fast feedback response, that the occurrence of rebound is determined by the ratio of the elimination rates, in a very similar way as for no feedback. However, for a slow feedback response, there will always be rebound. This result is illustrated with an example involving the drug efalizumab for patients with psoriasis. It is shown that slow feedback can be a plausible explanation for the observed rebound in this example.

Translational Pharmacokinetic/Pharmacodynamic Modeling of Tumor Growth Inhibition Supports Dose-Range Selection of the Anti-PD-1 Antibody Pembrolizumab

Pembrolizumab, a humanized monoclonal antibody against programmed death 1 (PD‐1), has a manageable safety profile and robust clinical activity against advanced malignancies. The lowest effective dose for evaluation in further dose‐ranging studies was identified by developing a translational model from preclinical mouse experiments. A compartmental pharmacokinetic model was combined with a published physiologically based tissue compartment, linked to receptor occupancy as the driver of observed tumor growth inhibition. Human simulations were performed using clinical pharmacokinetic data, literature values, and in vitro parameters for drug distribution and binding. Biological and mathematical uncertainties were included in simulations to generate expectations for dose response. The results demonstrated a minimal increase in efficacy for doses higher than 2 mg/kg. The findings of the translational model were successfully applied to select 2 mg/kg as the lowest dose for dose‐ranging evaluations.

Applied Concepts in PBPK Modeling: How to Build a PBPK/PD Model

The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.

The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms

In this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose.

Preclinical to Clinical Translation of Antibody-Drug Conjugates Using PK/PD Modeling: a Retrospective Analysis of Inotuzumab Ozogamicin

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model was used for preclinical to clinical translation of inotuzumab ozogamicin, a CD22-targeting antibody-drug conjugate (ADC) for B cell malignancies including non-Hodgkin's lymphoma (NHL) and acute lymphocytic leukemia (ALL). Preclinical data was integrated in a PK/PD model which included (1) a plasma PK model characterizing disposition and clearance of inotuzumab ozogamicin and its released payload N-Ac-γ-calicheamicin DMH, (2) a tumor disposition model describing ADC diffusion into the tumor extracellular environment, (3) a cellular model describing inotuzumab ozogamicin binding to CD22, internalization, intracellular N-Ac-γ-calicheamicin DMH release, binding to DNA, or efflux from the tumor cell, and (4) tumor growth and inhibition in mouse xenograft models. The preclinical model was translated to the clinic by incorporating human PK for inotuzumab ozogamicin and clinically relevant tumor volumes, tumor growth rates, and values for CD22 expression in the relevant patient populations. The resulting stochastic models predicted progression-free survival (PFS) rates for inotuzumab ozogamicin in patients comparable to the observed clinical results. The model suggested that a fractionated dosing regimen is superior to a conventional dosing regimen for ALL but not for NHL. Simulations indicated that tumor growth is a highly sensitive parameter and predictive of successful outcome. Inotuzumab ozogamicin PK and N-Ac-γ-calicheamicin DMH efflux are also sensitive parameters and would be considered more useful predictors of outcome than CD22 receptor expression. In summary, a multiscale, mechanism-based model has been developed for inotuzumab ozogamicin, which can integrate preclinical biomeasures and PK/PD data to predict clinical response.

Microtechnology-based organ systems and whole-body models for drug screening

After drug administration, the drugs are absorbed, distributed, metabolized, and excreted (ADME). Because ADME processes affect drug efficacy, various in vitro models have been developed based on the ADME processes. Although these models have been widely accepted as a tool for predicting the effects of drugs, the differences between in vivo and in vitro systems result in high attrition rates of drugs during the development process and remain a major limitation. Recent advances in microtechnology enable more accurate mimicking of the in vivo environment, where cellular behavior and physiological responses to drugs are more realistic; this has led to the development of novel in vitro systems, known as "organ-on-a-chip" systems. The development of organ-on-a-chip systems has progressed to include the reproduction of multiple organ interactions, which is an important step towards "body-on-a-chip" systems that will ultimately predict whole-body responses to drugs. In this review, we summarize the application of microtechnology for the development of in vitro systems that accurately mimic in vivo environments and reconstruct multiple organ models.

Approaching Pharmacometrics as a Paleontologist Would: Recovering the Links Between Drugs and the Body Through Reconstruction

Our knowledge of dinosaurs comes primarily from the fossil record. Notwithstanding the condition of these vestiges, paleontologists reconstruct early reptilian life by comparison to previously discovered specimens. When relics are missing, reasonable deductions are used to fill in the gaps.

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.