Implications of Immunogenicity Testing for Therapeutic Monoclonal Antibodies: A Quantitative Pharmacology Framework.

The interpretation of immunogenicity results for a mAb product and prediction of its clinical consequences remain difficult, despite enormous advances in methodologies and efforts toward the best practice for consistent data generation and reporting. To this end, the contribution from the clinical pharmacology discipline has been largely limited to comparing descriptively the pharmacokinetic (PK) profiles by antidrug antibodies (ADA) status or testing the significance of ADA as a covariate in a population PK setting, similar to the practice for small-molecule drugs in investigating the effect of an intrinsic/extrinsic factor on the drug disposition. There is a need for a mAb disposition framework that captures the dynamics of ADA formation and drug's interactions with the ADA and target as parts of the drug distribution and elimination. Here we describe such a framework and examine it against the PK, ADA, and clinical response data from a phase 3 trial in patients treated with adalimumab. The proposed framework offered a generalized understanding of how the dose, target affinity, and drug/ADA analyte forms affects the manifestation of ADA response with regard to its detections and alterations of drug disposition and effectiveness. Furthermore, as an example, its utility for dose considerations was demonstrated through predicting for late-stage trials of a PCSK9 inhibitor in terms of development in ADA incidence and titers, and consequences on the drug disposition, interaction with target, and downstream lowering effect on LDL-C.

PK/PD model-informed dose selection for oncology phase I expansion: Case study based on PF-06939999, a PRMT5 inhibitor.

The optimal dose for targeted oncology therapeutics is often not the maximum tolerated dose. Pharmacokinetic/pharmacodynamic (PK/PD) modeling can be an effective tool to integrate clinical data to help identify the optimal dose. This case study shows the utility of population PK/PD modeling in selecting the recommended dose for expansion (RDE) for the first-in-patient (FIP) study of PF-06939999, a small-molecule inhibitor of protein arginine methyltransferase 5. In the dose escalation part of the FIP trial (NCT03854227), 28 patients with solid tumors were administered PF-06939999 at 0.5 mg, 4 mg, 6 mg, or 8 mg once daily (q.d.) or 0.5 mg, 1 mg, 2 mg, 4 mg, or 6 mg twice daily (b.i.d.). Tolerability, safety, PK, PD biomarkers (plasma symmetrical dimethyl-arginine [SDMA]), and antitumor response were assessed. Semimechanistic population PK/PD modeling analyses were performed to characterize the time-courses of plasma PF-06939999 concentrations, plasma SDMA, and platelet counts collected from 28 patients. Platelet counts were evaluated because thrombocytopenia was the treatment-related adverse event with clinical safety concern. The models adequately described the PK, SDMA, and platelet count profiles both at individual and population levels. Simulations suggested that among a range of dose levels, 6 mg q.d. would yield the optimal balance between achieving the PD target (i.e., 78% reduction in plasma SDMA) and staying below an acceptable probability of developing grade ≥3 thrombocytopenia. As a result, 6 mg q.d. was selected as the RDE. The model-informed drug development approach informed the rational dose selection for the early clinical development of PF-06939999.

Joint Disposition Properties and Comprehensive Pharmacokinetic Characterization of Antibody-Drug Conjugates.

Antibody-drug conjugates (ADCs) comprise 3 distinct parts: a specific antibody carrier (mAb), a linker, and a cytotoxic payload. Typical pharmacokinetic (PK) characterization of ADCs remains fragmented using separate noncompartmental analyses (NCA) of individual analytes, offering little insight into the dynamic relationships among the ADC components, and the safety and efficacy implications. As a result, it is exceedingly difficult to compare ADCs in terms of favorable PK characteristics. Therefore, there is a need for characterizing ADCs using the joint disposition properties critical for understanding the fate of an ADC complex and clinical implications. In this communication, we describe 3 joint disposition metrics (JDMs) for integrated NCA of ADCs based on a combination of common analytes of ADC, payload, conjugated payload, and total mAb. These JDMs were derived, each in a simple form of a ratio between appropriate PK parameters of two analytes, from the presumed drug delivery scheme behind typical ADC designs, in terms of (1) linker stability, (2) therapeutic exposure ratio, and (3) effective drug-to-antibody ratio in vivo. The validity of the JDM-based PK characterization was examined against model-based analyses via their applications to 3 clinical candidates: PF-06650808, PF-06647020, and PF-06664178. For instance, the linker stability estimates for PF-06650808, PF-06647020, and PF-06664178 were 0.31, 0.14, and 0.096, respectively, from the JDM-based analyses vs. 0.23, 0.11, and 0.086 by the model-based approach. Additionally, the JDMs were estimated for a number of FDA-approved or otherwise well-documented ADCs, showing their utilities in comparing ADCs in terms of favorable PK characteristics.

Risk-Based Pharmacokinetic and Drug-Drug Interaction Characterization of Antibody-Drug Conjugates in Oncology Clinical Development: An International Consortium for Innovation and Quality in Pharmaceutical Development Perspective.

Antibody-drug conjugates (ADCs) represent a rapidly evolving area of drug development and hold significant promise. To date, nine ADCs have been approved by the US Food and Drug Administration (FDA). These conjugates combine the target specificity of monoclonal antibodies with the anticancer activity of small-molecule therapeutics (also referred to as payload). Due to the complex structure, three analytes, namely ADC conjugate, total antibody, and unconjugated payload, are typically quantified during drug development; however, the benefits of measuring all three analytes at later stages of clinical development are not clear. The cytotoxic payloads, upon release from the ADC, are considered to behave like small molecules. Given the relatively high potency and low systemic exposure of cytotoxic payloads, drug-drug interaction (DDI) considerations for ADCs might be different from traditional small molecule therapeutics. The International Consortium for Innovation and Quality in Pharmaceutical Development (IQ Consortium) convened an ADC working group to create an IQ ADC database that includes 26 ADCs with six unique payloads. The analysis of the ADC data in the IQ database, as well as nine approved ADCs, supports the strategy of pharmacokinetic characterization of all three analytes in early-phase development and progressively minimizing the number of analytes to be measured in the late-phase studies. The systemic concentrations of unconjugated payload are usually too low to serve as a DDI perpetrator; however, the potential for unconjugated payloads as a victim still exists. A data-driven and risk-based decision tree was developed to guide the assessment of a circulating payload as a victim of DDI.

A mechanistic pharmacokinetic model with drug and antidrug antibody interplay, and its application for assessing the impact of immunogenicity response on bioequivalence testing.

AIMS: Single-dose pharmacokinetic (PK) studies in healthy subjects have been the design of choice for bioequivalence determination for decades. This preference has been recently extended to PK similarity studies of proposed biosimilars. However, PK similarity studies can be complicated by the effect of immunogenicity response on drug disposition. The impact is exacerbated when there is an imbalance in host-specific immunological characteristics of subjects between the test and reference groups. Such complications remain poorly understood. The purpose of this communication is to show that the impact of immunogenicity response on PK similarity determination can be critical, using adalimumab as an example. METHODS: Data for adalimumab concentrations and immunogenicity response over 10 weeks were obtained from 133 healthy subjects receiving a 40 mg dose of Humira® in a PK similarity study. Also, a population PK model with a mechanistic construct for delineating the interplay between adalimumab disposition and antidrug antibodies response was utilized to estimate via simulation the probability that a PK similarity study would fail in typical study settings. RESULTS: The simulations showed that the immunogenicity response can have a profound impact on the outcome of PK similarity determination. As such, the probability of failing to achieve the similarity conclusion increased to 51.9%, from 13.8% in the absence of immunogenicity response. CONCLUSION: This study provides a model-based framework for better understanding of how a PK similarity study can be optimally designed and for interpretation of the outcome of PK similarity determination when the drug disposition is affected in the presence of immunogenicity response.

Bayesian analysis of a rat formaldehyde DNA-protein cross-link model.

As the initial effort in a multi-step uncertainty analysis of a biologically based cancer model for formaldehyde, a Markov chain Monte Carlo (MCMC) analysis was performed for a compartmental model that predicts DNA-protein cross-links (DPX) produced by formaldehyde exposure. The Bayesian approach represented by the MCMC analysis integrates existing knowledge of the model parameters with observed, formaldehyde-DPX-specific data, providing a statistically sound basis for estimating model output uncertainty. Uncertainty and variability were evaluated through a hierarchical structure, where interindividual variability was considered for all model parameters and that variability was assumed to be uncertain on population levels. The uncertainty of the population mean and that of the population variance were significantly reduced through the MCMC analysis. Our investigation highlights several issues that must be dealt with in many real-world analyses (e.g., issues of parameters' nonidentifiability due to limited data) while demonstrating the feasibility of conducting a comprehensive quantitative uncertainty evaluation. The current analysis can be viewed as a case study, for a relatively simple model, illustrating some of the constraints that analysts will face when applying Bayesian approaches to biologically or physiologically based models of increasing complexity.

Assessing kinetic determinants for metabolism and oral uptake of octamethylcyclotetrasiloxane (D4) from inhalation chamber studies.

The pharmacokinetics of octamethylcyclotetrasiloxane (D4), a highly lipophilic and well-metabolized volatile cyclic siloxane, are more complex than those of other volatile hydrocarbons. The purpose of the present study was to evaluate rate constants for saturable metabolism in the body, to estimate possible presystemic D4 clearance by respiratory-tract tissues, and to assess rate constants for uptake of D4 after oral dosing. These experiments provided the opportunity to refine current physiologically based pharmacokinetic (PBPK) models for D4 and to independently estimate key model parameters by sensitive inhalation methods. The PBPK model could only be fitted to gas uptake results when metabolic capacity was included in the respiratory-tract epithelium. The model simulations were highly sensitive to the parameter for total percent of whole-body metabolism allocated to the respiratory tract, with optimal fits observed with this value equal to 5%. Oral uptake of D4 was evaluated using both closed and open chamber concentration time-course studies after intubation of D4 in corn oil. Conclusions from the oral uptake studies were also verified by comparison with independent data sets for blood concentrations of D4 after oral dosing. The pharmacokinetic (PK) analysis of uptake from the gut and release from blood into chamber air results for oral doses from 10 to 300 mg D4/kg body weight were consistent with a combination of prolonged and slow uptake of D4 from the gastrointestinal tract and of reduced absorption at higher doses, as well as the extrahepatic clearance of D4 in pulmonary tissues. These closed chamber gas uptake studies provide a valuable confirmation of the conclusions reached in other pharmacokinetic studies and have uncovered a situation where closed chamber loss is highly sensitive to respiratory-tract clearance. This sensitivity largely arises from the unusual characteristics of D4: high-affinity metabolic clearance and low blood:air partitioning.

Bayesian estimation of pharmacokinetic and pharmacodynamic parameters in a mode-of-action-based cancer risk assessment for chloroform.

Chloroform is a carcinogen in rodents and its carcinogenicity is secondary to events associated with cytotoxicity and regenerative cell proliferation. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model that links the processes of chloroform metabolism, reparable cell damage, cell death, and regenerative cellular proliferation was developed to support a new cancer dose-response assessment for chloroform. Model parameters were estimated using Markov Chain Monte Carlo (MCMC) analysis in a two-step approach: (1) metabolism parameters for male and female mice and rats were estimated against available closed chamber gas uptake data; and (2) PD parameters for each of the four rodent groups were estimated from hepatic and renal labeling index data following inhalation exposures. Subsequently, the resulting rodent PD parameters together with literature values for human age-dependent physiological and metabolism parameters were used to scale up the rodent model to a human model. The human model was used to predict exposure conditions under which chloroform-mediated cytolethality is expected to occur in liver and kidney of adults and children. Using the human model, inhalation Reference Concentrations (RfCs) and oral Reference Doses (RfDs) were derived using an uncertainty factor of 10. Based on liver and kidney dose metrics, the respective RfCs were 0.9 and 0.09 ppm; and the respective RfDs were 0.4 and 3 mg/kg/day.

Development of a screening approach to interpret human biomonitoring data on volatile organic compounds: reverse dosimetry on biomonitoring data for trichloroethylene.

A screening approach is developed for volatile organic compounds (VOCs) to estimate exposures that correspond to levels measured in fluids and/or tissues in human biomonitoring studies. The approach makes use of a generic physiologically-based pharmacokinetic (PBPK) model coupled with exposure pattern characterization, Monte Carlo analysis, and quantitative structure property relationships (QSPRs). QSPRs are used for VOCs with minimal data to develop chemical-specific parameters needed for the PBPK model. The PBPK model is capable of simulating VOC kinetics following multiple routes of exposure, such as oral exposure via water ingestion and inhalation exposure during shower events. Using published human biomonitoring data of trichloroethylene (TCE), the generic model is evaluated to determine how well it estimates TCE concentrations in blood based on the known drinking water concentrations. In addition, Monte Carlo analysis is conducted to characterize the impact of the following factors: (1) uncertainties in the QSPR-estimated chemical-specific parameters; (2) variability in physiological parameters; and (3) variability in exposure patterns. The results indicate that uncertainty in chemical-specific parameters makes only a minor contribution to the overall variability and uncertainty in the predicted TCE concentrations in blood. The model is used in a reverse dosimetry approach to derive estimates of TCE concentrations in drinking water based on given measurements of TCE in blood, for comparison to the U.S. EPA's Maximum Contaminant Level in drinking water. This example demonstrates how a reverse dosimetry approach can be used to facilitate interpretation of human biomonitoring data in a health risk context by deriving external exposures that are consistent with a biomonitoring data set, thereby permitting comparison with health-based exposure guidelines.

Reverse dosimetry: interpreting trihalomethanes biomonitoring data using physiologically based pharmacokinetic modeling.

Biomonitoring data provide evidence of exposure of environmental chemicals but are not, by themselves, direct measures of exposure. To use biomonitoring data in understanding exposure, physiologically based pharmacokinetic (PBPK) modeling can be used in a reverse dosimetry approach to assess a distribution of exposures possibly associated with specific blood or urine levels of compounds. Reverse dosimetry integrates PBPK modeling with exposure pattern characterization, Monte Carlo analysis, and statistical tools to estimate a distribution of exposures that are consistent with biomonitoring data in a population. The present study used an existing PBPK model for chloroform as a generic framework to develop PBPK models for other trihalomethanes (THMs). Using Monte Carlo sampling techniques, probabilistic information about pharmacokinetics and exposure patterns was included to estimate distributions of THMs concentrations in blood in relation to various exposure patterns in a diverse population. In addition, the possibility of inhibition of hepatic metabolism among THMs was evaluated under the scenarios of household exposure. These studies demonstrated how PBPK modeling can be used as a tool to estimate a population distribution of exposures that could have resulted in particular biomonitoring results. When toxicity level is known, this tool can also be used to estimate proportion of population above levels associated with health risk.

Use of a physiologically based pharmacokinetic model to identify exposures consistent with human biomonitoring data for chloroform.

Biomonitoring data provide evidence of human exposure to environmental chemicals by quantifying the chemical or its metabolite in a biological matrix. To better understand the correlation between biomonitoring data and environmental exposure, physiologically based pharmacokinetic (PBPK) modeling can be of use. The objective of this study was to use a combined PBPK model with an exposure model for showering to estimate the intake concentrations of chloroform based on measured blood and exhaled breath concentrations of chloroform. First, the predictive ability of the combined model was evaluated with three published studies describing exhaled breath and blood concentrations in people exposed to chloroform under controlled showering events. Following that, a plausible exposure regimen was defined combining inhalation, ingestion, and dermal exposures associated with residential use of water containing typical concentrations of chloroform to simulate blood and exhaled breath concentrations of chloroform. Simulation results showed that inhalation and dermal exposure could contribute substantially to total chloroform exposure. Next, sensitivity analysis and Monte Carlo analysis were performed to investigate the sources of variability in model output. The variability in exposure conditions (e.g., shower duration) was shown to contribute more than the variability in pharmacokinetics (e.g., body weight) to the predicted variability in blood and exhaled breath concentrations of chloroform. Lastly, the model was used in a reverse dosimetry approach to estimate distributions of exposure consistent with concentrations of chloroform measured in human blood and exhaled breath.

Dose-incidence modeling: consequences of linking quantal measures of response to depletion of critical tissue targets.

In developing mechanistic PK-PD models, incidence of toxic responses in a population has to be described in relation to measures of biologically effective dose (BED). We have developed a simple dose-incidence model that links incidence with BED for compounds that cause toxicity by depleting critical cellular target molecules. The BED in this model was the proportion of target molecule adducted by the dose of toxic compound. Our modeling approach first estimated the proportion depleted for each dose and then calculated the tolerance distribution for toxicity in relation to either administered dose or log of administered dose. We first examined cases where the mean of the tolerance distribution for toxicity occurred when a significant proportion of target had been adducted (i.e., more than half). When a normal distribution was assumed to exist for the relationship of incidence and BED, the tolerance distribution based on administered dose for these cases becomes asymmetrical and logarithmic transformations of the administered dose axis lead to a more symmetrical distribution. These linked PK-PD models for tissue reactivity, consistent with conclusions from other work for receptor binding models (Lutz et al., 2005), indicate that log normal distributions with administered dose may arise from normal distributions for BED and nonlinear kinetics between BED and administered dose. These conclusions are important for developing biologically based dose response (BBDR) models that link incidences of toxicity or other biological responses to measures of BED.

A novel, sensitive method for determining benzo[a]pyrene-diones using high-performance liquid chromatography with post-column zinc reduction.

A novel and sensitive high-performance liquid chromatography (HPLC) method was developed to analyze dione metabolites of benzo[a]pyrene (BaP). Because BaP-diones do not fluoresce, detection of low concentrations is difficult to achieve when analyzing these chemicals with a simple HPLC system. We developed a method to increase the detection sensitivities for BaP-diones using reduction by zinc after the chromatographic separation. A post-column zinc reducer was used to convert BaP-diones, in-line, to their corresponding fluorescent BaP-hydroquinones, which can be measured by fluorescence detection with high sensitivity. With 20-muL injections, the limits of detection for the BaP-diones tested (BaP-1,6-dione, BaP-3,6-dione, and BaP-6,12-dione) were all below 1.0 nM. In addition to the high detection sensitivity, this HPLC method provides a wide linear dynamic range for BaP-dione detection (1.0-220 nM). We also studied the extraction recovery of BaP-diones from recombinant human cytochrome P450 and epoxide hydrolase. To demonstrate the application of this method, the kinetics of BaP-dione formation was studied by incubating BaP with these recombinant enzymes. The present method enhances the detection sensitivity for BaP-diones by more than two orders of magnitude compared with traditional ultraviolet detection. Moreover, the method avoids the time-consuming derivatization or reduction steps required by other methods.

Chemical mixture toxicology: from descriptive to mechanistic, and going on to in silico toxicology.

Because of the pioneering vision of certain leaders in the biomedical field, the last two decades witnessed rapid advances in the area of chemical mixture toxicology. Earlier studies utilized conventional toxicology protocol and methods, and they were mainly descriptive in nature. Two good examples might be the parallel series of studies conducted by the U.S. National Toxicology Program and TNO in The Netherlands, respectively. As a natural course of progression, more and more sophistication was incorporated into the toxicology studies of chemical mixtures. Thus, at least the following seven areas of scientific achievements in chemical mixture toxicology are evident in the literature: (a) the application of better and more robust statistical methods; (b) the exploration and incorporation of mechanistic bases for toxicological interactions; (c) the application of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling; (d) the studies on more complex chemical mixtures; (e) the use of science-based risk assessment approaches; (f) the utilization of functional genomics; and (g) the application of technology. Examples are given for the discussion of each of these areas. Two important concepts emerged from these studies and they are: (1) dose-dependent toxicologic interactions; and (2) "interaction thresholds". Looking into the future, one of the most challenging areas in chemical mixture research is finding the answer to the question "when one tries to characterize the health effects of chemical mixtures, how does one deal with the infinite number of combination of chemicals, and other possible stressors?" Undoubtedly, there will be many answers from different groups of researchers. Our answer, however, is first to focus on the finite (biological processes) rather than the infinite (combinations of chemical mixtures and multiple stressors). The idea is that once we know a normal biological process(es), all stimuli and insults from external stressors are merely perturbations of the normal biological process(es). The next step is to "capture" the biological process(es) by integrating the recent advances in computational technology and modern biology. Here, the computer-assisted Reaction Network Modeling, linked with PBPK modeling, offers a ray of hope to dealing with the complex biological systems.

Application of biologically based computer modeling to simple or complex mixtures.

The complexity and the astronomic number of possible chemical mixtures preclude any systematic experimental assessment of toxicology of all potentially troublesome chemical mixtures. Thus, the use of computer modeling and mechanistic toxicology for the development of a predictive tool is a promising approach to deal with chemical mixtures. In the past 15 years or so, physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling has been applied to the toxicologic interactions of chemical mixtures. This approach is promising for relatively simple chemical mixtures; the most complicated chemical mixtures studied so far using this approach contained five or fewer component chemicals. In this presentation we provide some examples of the utility of PBPK/PD modeling for toxicologic interactions in chemical mixtures. The probability of developing predictive tools for simple mixtures using PBPK/PD modeling is high. Unfortunately, relatively few attempts have been made to develop paradigms to consider the risks posed by very complex chemical mixtures such as gasoline, diesel, tobacco smoke, etc. However, recent collaboration between scientists at Colorado State University and engineers at Rutgers University attempting to use reaction network modeling has created hope for the possible development of a modeling approach with the potential of predicting the outcome of toxicology of complex chemical mixtures. We discuss the applications of reaction network modeling in the context of petroleum refining and its potential for elucidating toxic interactions with mixtures.

BioMOL: a computer-assisted biological modeling tool for complex chemical mixtures and biological processes at the molecular level.

A chemical engineering approach for the rigorous construction, solution, and optimization of detailed kinetic models for biological processes is described. This modeling capability addresses the required technical components of detailed kinetic modeling, namely, the modeling of reactant structure and composition, the building of the reaction network, the organization of model parameters, the solution of the kinetic model, and the optimization of the model. Even though this modeling approach has enjoyed successful application in the petroleum industry, its application to biomedical research has just begun. We propose to expand the horizons on classic pharmacokinetics and physiologically based pharmacokinetics (PBPK), where human or animal bodies were often described by a few compartments, by integrating PBPK with reaction network modeling described in this article. If one draws a parallel between an oil refinery, where the application of this modeling approach has been very successful, and a human body, the individual processing units in the oil refinery may be considered equivalent to the vital organs of the human body. Even though the cell or organ may be much more complicated, the complex biochemical reaction networks in each organ may be similarly modeled and linked in much the same way as the modeling of the entire oil refinery through linkage of the individual processing units. The integrated chemical engineering software package described in this article, BioMOL, denotes the biological application of molecular-oriented lumping. BioMOL can build a detailed model in 1-1,000 CPU sec using standard desktop hardware. The models solve and optimize using standard and widely available hardware and software and can be presented in the context of a user-friendly interface. We believe this is an engineering tool with great promise in its application to complex biological reaction networks.