Dosing Strategy of Immunoglobulin (IgG) Replacement Therapies in Obese and Overweight Patients with Primary Immunodeficiency Diseases (PIDDs): A Meta-Analysis of Clinical Trials.

The current dosing strategy of immune globulin products for the treatment of primary immunodeficiency diseases (PIDDs) in the USA is based on total body weight (BW). The aim of our study was to assess the relationship between dose and trough level, and to determine whether an alternative dosing strategy should be considered for patients who are overweight or obese. We analyzed data in a total of 533 patients from 11 studies. We modeled the relationship between trough level and dose per week using a linear mixed model. We used an over-dispersed Poisson model to model the relationship between infection and trough level. In these analyses, we then combined the study-specific treatment effects using a random-effect or fixed-effect model. The mean administered dose per week was 9.77, 14.00, or 18.17 g in patients who were normal weight, overweight, or obese, respectively. Compared with a patient of normal weight, a 1 g increase in dose per week in a patient who was overweight was associated with a smaller increase in the trough level, 0.08 g/L less (95%CI -0.14 to -0.03 g/L), and a 1 g increase in dose per week in a patient who was obese was associated with a much smaller increase in trough level, 0.01 g/L less (95% CI -0.07 to 0.06 g/L). Last, for a 1 unit (g/L) increase in trough level, the expected number of infections remained the same, with a multiplicative factor of 1.01 (95%CI 0.98-1.04). Overall, we found no compelling evidence to justify a reconsideration of the current dosing strategy based on total BW for patients with PIDDs who are overweight or obese.

Pharmacokinetics of antibodies during pregnancy: General pharmacokinetics and pregnancy related physiological changes (Part 1).

Pharmacokinetics (PK) studies are important to determine a safe and effective dose of both small and large molecule drugs. Intrinsic factors such as pregnancy can substantially alter the PK of a drug. Several PK studies have been published for small molecules administered during pregnancy, but such investigations are scarce for macromolecules including monoclonal and polyclonal antibodies. In this part 1 of 2 reviews, we first provide a general description of macromolecule drugs, the PK differences with small molecules, and current knowledge on their absorption, distribution, metabolism and elimination in non-pregnant subjects. We then review in detail the physiological changes during pregnancy. While some of the physiologic adaptions of pregnancy, for example increased plasma volume and cardiac output, are expected to impact PK of antibody therapeutics, the effects of others, such as increased GFR and altered immune responses are not fully understood. We conclude that further investigations are needed to fully elucidate how pregnancy can impact PK properties of macromolecules.

Pharmacokinetics of antibodies during Pregnancy: Impact of pregnancy on the pharmacokinetics of antibodies (Part 2).

In Part 1, we provided a general description of macromolecules, pharmacokinetics (PK) characteristics in non-pregnant subjects, and the physiological changes during pregnancy. Here we further elaborate on the impact of pregnancy on the PK of antibodies through illustrative case studies (immunoglobulins, infliximab, adalimumab and eculizumab). Using published data from nonclinical and clinical studies, we present measured or calculated PK parameters from pregnant subjects comparing with data from non-pregnant subjects, if available. Due to the paucity of PK data evaluating PK of antibodies during pregnancy, we also provide examples of PK studies for small molecules. Finally, we draw conclusions on the nature and direction of PK changes for both antibodies and small molecules as well as provide recommendations for areas that would benefit from further studies.

Spreadsheet-Based Minimal Physiological Models for the Prediction of Clearance of Therapeutic Proteins in Pediatric Patients.

There is a growing interest in the use of physiologically based pharmacokinetic (PBPK) models as clinical pharmacology drug development tools. In PBPK modeling, not every organ or physiological parameter is required, leading to the development of a minimal PBPK (mPBPK) model, which is simple and efficient. The objective of this study was to streamline mPBPK modeling approaches and enable straightforward prediction of clearance of protein-based products in children. Four mPBPK models for scaling clearance from adult to children were developed and evaluated on Excel spreadsheets using (1) liver and kidneys; (2) liver, kidneys, and skin; (3) liver, kidneys, skin, and lymph; and (4) interstitial, lymph, and plasma volume. There were 35 therapeutic proteins with a total of 113 observations across different age groups (premature neonates to adolescents). For monoclonal and polyclonal antibodies, more than 90% of observations were within a 0.5- to 2-fold prediction error for all 4 methods. For nonantibodies, 79% to 100% of observations were within the 0.5- to 2-fold prediction error for the 4 different methods. Methods 1 and 4 provided the best results, >90% of the total observations were within the 0.5- to 2-fold prediction error for all 3 classes of protein-based products across a wide age range. The precision of clearance prediction was comparatively lower in children ≤2 years of age vs older children (>2 years of age) with methods 1 and 4 predicting 80% to 100% and 75% to 90% of observations within the 0.5- to 2-fold prediction error, respectively. The results of the study indicated that mPBPK models can be developed on spreadsheets, with acceptable performance for prediction of clearance.

Opportunities and challenges for applying model-informed drug development approaches to gene therapies.

As part of the US Food and Drug Administration (FDA)'s Prescription Drug User Fee Act (PDUFA) VI commitments, the Center for Biologics Evaluation and Research (CBER) and Center for Drug Evaluation and Research (CDER) are conducting a model-informed drug development (MIDD) pilot program. Sponsor(s) who apply and are selected will be granted meetings that aim to facilitate the application of MIDD approaches throughout the product development lifecycle and the regulatory process. Due to their complex mechanisms of action and limited clinical experience, cell and gene therapies have the potential to benefit from the application of MIDD methods, which may facilitate their safety and efficacy evaluations. Leveraging data that are generated from all stages of drug development into appropriate modeling and simulation techniques that inform decisions remains challenging. Additional discussions regarding the application of quantitative modeling approaches to drug development decisions, such as through the MIDD pilot program, may be crucial for both the sponsor(s) and regulatory review teams. Here, we share some perspectives on the opportunities and challenges for utilizing MIDD approaches for product review, which we hope will encourage investigators to publish their experiences and application of MIDD in gene therapy product development.

Dosing Considerations for Antibodies Against COVID-19.

At present, no cure is available for COVID-19 but vaccines, antiviral drugs, immunoglobulins, or the combination of immunoglobulins with antiviral drugs have been suggested and are in clinical trials. The purpose of this paper is to discuss the role of a pharmacokinetic and viral load analysis as a basis for adjusting immunoglobulin dosing to treat COVID-19. We reviewed the pre-clinical and clinical literature that describes the impact of a high antigen load on pharmacokinetic data following antibody treatment. Representative examples are provided to illustrate the effect of high viral and tumor loads on antibody clearance. We then highlight the implications of these factors for facilitating the development and dosing of hyperimmune anti-SARS CoV2 immunoglobulin. Both nonclinical and clinical examples indicate that high antigen loads, whether they be viral, bacterial, or tumoral in origin, result in increased clearance and decreased area under the curve and half-life of antibodies. A dosing strategy that matches the antigen load can be achieved by giving initially high doses and adjusting the frequency of dosing intervals based on pharmacokinetic parameters. We suggest that study design and dose selection for immunoglobulin products for the treatment of COVID-19 require special considerations such as viral load, antibody-virus interaction, and dosing adjustment based on the pharmacokinetics of the antibody.

Comparing clotting factors attributes across different methods of preference elicitation in haemophilia patients.

INTRODUCTION: Emerging, systematic approaches for capturing patient input, such as preference elicitation, can provide valuable information for the benefit-risk assessment of medical products for treating bleeding disorders, such as haemophilia. AIM: This study aims to identify existing and develop new methods to capture, rank and summarize preference scores for clotting factor therapies. METHODS: Haemophilia patient preference data were compiled from studies identified through literature review and publicly available US FDA patient-focused drug development meeting documents. Text mining was performed to identify major themes across studies. A standardized preference score was estimated and aggregated. RESULTS: Ten preference studies that employed qualitative (n = 3), and quantitative methods (n = 7) met the inclusion criteria. Text mining of qualitative and quantitative studies revealed similar themes as the standardized preference attribute importance. We found that seven quantitative studies employed discrete choice experiments (DCE)/conjoint analysis (CA) and examined a range of 5-12 attributes. For DCE/CA studies published prior to 2014 (n = 4), safety attributes (inhibitor and viral safety) were among the most important attributes, accounting for ~46% of the total utility measured. DCE/CA studies published after 2014 (n = 3) focused on frequency of infusion and reduction of bleeding risk, accounting for ~67% of the total utility. Interestingly, two studies that used different preference elicitation approaches (DCE and a monadic conjoint approach) both ranked infusion frequency as the most important attribute. CONCLUSIONS: Although there are few published patient preference studies for haemophilia, the results of this study can be viewed in the larger context of enhancing scientific methods of incorporating patient input in medical product development.

Considerations for Optimizing Dosing of Immunoglobulins Based on Pharmacokinetic Evidence.

Immunoglobulins (IGs) are widely used for the treatment of immunodeficiency syndromes and several autoimmune diseases. In neonates, IGs have been used for the treatment of alloimmune thrombocytopenia, in neonatal infections and in the rare cases of neonatal Kawasaki disease. This review aims to examine the various dosing regimens of IGs following intravenous (IV) and subcutaneous (SC) administration, pharmacokinetics (PK) of IGs, and the importance of trough values for the prevention of infections in patients with primary immune deficiency (PID). The review also focuses on the mechanism of catabolism of IGs and the impact on the half-life of IGs. Data and reviews were obtained from the literature and the FDA package inserts. The authors suggest that for dosing, the PK of IGs should be evaluated on the baseline-corrected concentrations since this approach provides an accurate estimate of half-life and clearance of IGs. We also suggest employing clearance as a primary PK parameter for dosing determination of IGs. We suggest that IV dosing would be more effective if given more frequently to adjust for the increased clearance at high doses and because the baseline-corrected half-life is much shorter than the baseline-uncorrected half-life. Regarding SC administration, the dose should be adjusted based on the absolute bioavailability (determined against IV dosing) of the product. Finally, we highlight clinical and PK data gaps for optimum and individualized dosing of IGs.

Model-Based Evaluation of Linear Limited and Bayesian Sparse Sampling for Therapeutic Monitoring of Recombinant Coagulation Factor IX.

Dosing of coagulation factor products is mainly determined based on a patient's body weight; however, several studies have reported high interindividual variability in their pharmacokinetics (PK). The objective of this study was to develop and evaluate 2 sparse sampling methods for the estimation of AUC of recombinant factor IX (BeneFIX) as proof of concept for dose individualization. A population pharmacokinetic model was used to generate the plasma factor IX activity-versus-time data. The linear limited sampling model (LLSM) was developed based on the correlation of factor IX activity versus AUC0-72 hours following screening of several blood sampling times in adolescent and adult subjects (n = 90 subjects). Factor IX trough concentrations were predicted from a relationship established from AUC versus factor IX activity measured 72 hours postdosing. Using the best selected sampling time, the LLSM and Bayesian model were validated in separate data sets (n = 75 subjects). Using the LLSM and Bayesian analysis, a blood sample at 24 hours predicted AUC with bias and root mean square error < 5% and < 15%, respectively. The predicted trough concentrations were ≥1 IU/dL in 99% and 100% of subjects by the LLSM and Bayesian model, respectively. The average factor IX dose for a target AUC of 800 IU·h/dL was 61, 60, and 63 IU/kg using the extensive (reference), LLSM and Bayesian model, respectively. Overall, the AUC, trough concentrations and individualized dosing of recombinant factor IX could be reasonably predicted using the LLSM and Bayesian model.

Considerations for pharmacokinetic assessment of immunoglobulins: Gammagard in very low birth weight neonates with and without baseline-correction.

BACKGROUND: Immunoglobulins are widely used across multiple therapeutic areas such as immunodeficiency syndromes, infection and autoimmune diseases. The pharmacokinetics (PK) of immunoglobulins are well characterized in adults, but very little is known about the PK of immunoglobulins in neonates and infants. OBJECTIVE: The objective of the present study was to characterize the PK of Gammagard, an immunoglobulin, in very low birth weight preterm neonates. METHOD: Gammagard concentration-time data from very low birth weight neonates (bodyweight range 0.78-1.38 kg, n = 20) following intravenous administration of 500 mg/kg and 750 mg/kg were obtained from the literature. The data were analyzed with and without baseline correction using extensive blood samples (8 blood samples). Model-independent (non-compartmental) analysis was used to characterize the PK of Gammagard. RESULTS: Based on uncorrected baseline concentration-time data, the clearance and half-life of Gammagard were 3.1 ± 0.7 mL/day and 22 ± 6 days, respectively. Based on corrected baseline concentration-time data, the clearance and half-life of Gammagard were 20.2 ± 7.4 mL/day and 5.3 ± 2.2 days, respectively. CONCLUSION: The dose of immunoglobulins should be adjusted based on the PK of baseline corrected rather than baseline uncorrected profiles because baseline corrected PK parameters especially half-life reconciles with PK principles.

Population pharmacokinetics of immunoglobulin intravenous preparation in very low birth weight neonates.

BACKGROUND: Immunoglobulin products are widely used across multiple therapeutic areas such as immunodeficiency syndromes, infection and autoimmune diseases. The pharmacokinetics (PK) of immunoglobulins are well characterized in adults, but very little is known about the PK of immunoglobulins in neonates and infants (<2 years of age). OBJECTIVE: The objectives of the present study were: (1) characterize the PK of immunoglobulin intravenous preparation using model-independent (non-compartmental analysis), and (2) develop and evaluate a population PK model with extensive blood samples (8 blood samples) and sparse blood samples (2-3 blood samples). METHOD: Immunoglobulin G (IgG) concentration versus time data from very low birth weight neonates (n = 20) following intravenous administration were analyzed using nonlinear mixed effect modeling and non-compartmental approaches. Population pharmacokinetic models were developed from extensive and sparse sampling schemes. Models were evaluated based on the difference in objective function, goodness-of-fit plots and simulation based visual predictive check analysis. RESULTS: A non-compartmental analysis of IgG from neonates (bodyweight range 0.78-1.38 kg) indicated an average clearance of 3.0 ± 2.1 mL/day and volume of distribution at steady state 68 ± 25 mL. The population pharmacokinetic model from extensive sampling adequately described concentration- time data with mean clearance (2.7 mL/day), volume of central compartment (8.7 mL) and peripheral compartment (60 mL). The clearance and volume of distribution estimates using sparse sampling model (1 pre-and 2 post-dose blood samples) were comparable with extensive sampling. CONCLUSION: Our study provides important bridging data in scaling PK and dosing of immunoglobulins across a wide age range.

Comparative pharmacokinetic and biodistribution study of two distinct squalene-containing oil-in-water emulsion adjuvants in H5N1 influenza vaccines.

BACKGROUND: In recent years, there has been great interest from academia, industry and government scientists for an increased understanding of the mode of action of vaccine adjuvants to characterize the safety and efficacy of vaccines. In this context, pharmacokinetic (PK) and biodistribution studies are useful for quantifying the concentration of vaccine adjuvants in mechanistically or toxicologically relevant target tissues. METHODS: In this study, we conducted a comparative analysis of the PK and biodistribution profile of radiolabeled squalene for up to 336 h (14 days) after intramuscular injection of mice with adjuvanted H5N1 influenza vaccines. The evaluated adjuvants included an experimental-grade squalene-in-water (SQ/W) emulsion (AddaVax®) and an adjuvant system (AS03®) that contained squalene and α-tocopherol in the oil phase of the emulsion. RESULTS: The half-life of the initial exponential decay from quadriceps muscle was 1.5 h for AS03 versus 12.9 h for AddaVax. At early time points (1-6 h), there was about a 10-fold higher concentration of labeled squalene in draining lymph nodes following AS03 injection compared to AddaVax. The area-under-concentration curve up to 336 h (AUC0-336hr) and peak concentration of squalene in spleen (immune organ) was about 1.7-fold higher following injection of AS03 than AddaVax. The peak systemic tissue concentration of squalene from the two adjuvants, with or without antigen, remained below 1% of injected dose for toxicologically relevant target tissues, such as spinal cord, brain, and kidney. The pharmacokinetics of AS03 was unaffected by the presence of H5N1 antigen. CONCLUSIONS: This study demonstrates a rapid decline of AS03 from the quadriceps muscles of mice as compared to conventional SQ/W emulsion adjuvant, with an increased transfer to mechanistically relevant tissues such as local lymph nodes. Systemic tissue exposure to potential toxicological target tissues was very low.

TCPro: an In Silico Risk Assessment Tool for Biotherapeutic Protein Immunogenicity.

Most immune responses to biotherapeutic proteins involve the development of anti-drug antibodies (ADAs). New drugs must undergo immunogenicity assessments to identify potential risks at early stages in the drug development process. This immune response is T cell-dependent. Ex vivo assays that monitor T cell proliferation often are used to assess immunogenicity risk. Such assays can be expensive and time-consuming to carry out. Furthermore, T cell proliferation requires presentation of the immunogenic epitope by major histocompatibility complex class II (MHCII) proteins on antigen-presenting cells. The MHC proteins are the most diverse in the human genome. Thus, obtaining cells from subjects that reflect the distribution of the different MHCII proteins in the human population can be challenging. The allelic frequencies of MHCII proteins differ among subpopulations, and understanding the potential immunogenicity risks would thus require generation of datasets for specific subpopulations involving complex subject recruitment. We developed TCPro, a computational tool that predicts the temporal dynamics of T cell counts in common ex vivo assays for drug immunogenicity. Using TCPro, we can test virtual pools of subjects based on MHCII frequencies and estimate immunogenicity risks for different populations. It also provides rapid and inexpensive initial screens for new biotherapeutics and can be used to determine the potential immunogenicity risk of new sequences introduced while bioengineering proteins. We validated TCPro using an experimental immunogenicity dataset, making predictions on the population-based immunogenicity risk of 15 protein-based biotherapeutics. Immunogenicity rankings generated using TCPro are consistent with the reported clinical experience with these therapeutics.

Clinical Pharmacology Review of Plasma-derived and Recombinant Protein Products: CBER Experience and Perspectives on Model-Informed Drug Development.

INTRODUCTION: Clinical pharmacology studies are one of the major types of regulatory data submitted for review of therapeutic proteins regulated by the Center for Biologics Evaluation and Research (CBER). AIM: The primary objective of the current study is to provide an overview of the role of clinical pharmacology including pharmacokinetics (PK), pharmacodynamics (PD) and exposure-response analysis at CBER. Furthermore, we aim to provide a baseline estimate for the use of quantitative clinical pharmacology studies prior to implementation of FDA's model-informed drug development (MIDD) pilot programme. METHODS: We survey original Biologics License Applications (BLAs) for plasma-derived and related recombinant therapeutic protein products approved by CBER/FDA (2008-2017). RESULTS: There were 37 original BLAs that met our inclusion criteria, and 34 of these products (92%) contained human PK data as part of the biological licensing. The products were broadly classified as coagulation factors (54%), IgG and related proteins (24%), and other therapeutic proteins (22%). Coagulation factor VIII and IX products constitute 32% of the BLAs and indicated for treatment of haemophilia A and B, respectively. Twelve products (35%) used model-based approaches (population PK/PD and exposure-response). Over the past 5 years (2013 to 2017), there is a trend for increased application of MIDD approaches as compared to the previous cohort years (2008 to 2012). CONCLUSION: In conclusion, clinical pharmacology has played a major role in regulatory review of plasma-derived products, and we expect that the application of quantitative methods will further evolve for these products under the FDA MIDD programme.

A Comparative Study Between Allometric Scaling and Physiologically Based Pharmacokinetic Modeling for the Prediction of Drug Clearance From Neonates to Adolescents.

The objective of this study was to compare the predictive performance of an allometric model with that of a physiologically based pharmacokinetic (PBPK) model to predict clearance or area under the concentration-time curve (AUC) of drugs in subjects from neonates to adolescents. From the literature, 10 studies were identified in which clearance or AUC of drugs from neonates to adolescents was predicted by PBPK models. In these published studies, drugs were given to children either by intravenous or oral route. The allometric model was an age-dependent exponent (ADE) model for the prediction of clearance across the age groups. The predicted clearance or AUC values from the PBPK and ADE models were compared with the experimental values. The acceptable prediction error was the percentage of subjects within an 0.5- to 2-fold or 0.5- to 1.5-fold prediction error. There were 73 drugs with a total of 372 observations. From PBPK and allometric models, 91.1% and 90.6% of observations were within 0.5- to 2-fold prediction error, respectively. For children ≤2 years old (n = 130), PBPK and allometric models had 89% and 87% of observations within the 0.5- to 2-fold prediction error, respectively. This study indicates that the predictive power of PBPK and allometric models was essentially similar for the prediction of clearance or AUC in pediatric subjects ranging from neonates to adolescents.

Age- and Bodyweight-dependent Allometric Exponent Model for Scaling Clearance and Maintenance Dose of Theophylline From Neonates to Adults.

BACKGROUND: In population pharmacokinetic modeling, bodyweight is often incorporated as an important covariate using fixed (0.75) or single-exponent model. In recent years, several variations of allometric models have been suggested for the prediction of drug clearance across a wide age range. The objective of this study is to develop and evaluate single-exponent, bodyweight-dependent allometric exponent (BDE), age-dependent exponent (ADE), and segmented regression models for predicting clearance and maintenance dose of theophylline. METHODS: The BDE model was described by the following equation: (Equation is included in full-text article.), where L × BW defines the BDE for clearance. The coefficient and the exponents L and M were estimated. The ADE model consisted of several empirical exponents based on age and ranged from 0.75 (children >5 years and adults) to 1.2 (premature neonates). Data for model development and validation were based on 52 subjects each. RESULTS: All structural and statistical parameters were estimated with acceptable precision for single-exponent and BDE models (<30%); however, the BDE model was superior in describing theophylline clearance across a wide age range for the training data. The segmented regression model on log-transformed data also adequately described theophylline clearance. When models were evaluated with validation data, a single-exponent model overpredicted clearance and dosing rate in premature neonates and adults with a mean prediction error of ≥50%. For premature neonates and adults, mean clearance and dosing rate were predicted within a 30% prediction error using the BDE, ADE, and segmented models. CONCLUSIONS: This study demonstrates that the BDE, ADE, and segmented models performed better than a single-exponent model for predicting clearance and dose of theophylline across a wide age range.

Prediction of tissue concentrations of monoclonal antibodies in mice from plasma concentrations.

The objectives of this study were to develop and evaluate allometric methods for predicting tissue-to-plasma partition coefficients (Kp) in mice from experimentally determined in-vivo volume of distribution at steady state (Vss) for monoclonal antibodies (mAbs). The Vss was allometrically predicted (using a fixed exponent 1.0 or 0.9) in a given tissue of the mice. The Kp was predicted using Vss and tissue specific physiological parameters. In total, Kp values were predicted for 20 mAbs, 121 tissues, and 665 tissue concentrations. The predicted Kp values and tissue concentrations were compared with the experimental results as well as an empirically predicted antibody biodistribution coefficient (ABC). Comparison of the predicted Kp values by the two proposed methods with experimentally determined Kp values indicated that 64-75% of the predicted Kp values were within two-fold prediction error. For 665 tissue concentrations, 63%, 74%, and 48% tissue concentration ratio were within 0.5-2 fold prediction error by exponent 1.0, exponent 0.9, and ABC, respectively. The proposed allometric methods are better than ABC method for the prediction of tissue Kp values and tissue concentrations. The proposed methods can reasonably predict tissue concentrations of mAbs using plasma concentration gathered at early stage of biologics development.

Multistep Unified Models Using Prior Knowledge for the Prediction of Drug Clearance in Neonates and Infants.

Allometric approaches are widely used for interspecies scaling for the prediction of pharmacokinetic (PK) parameters during drug development. The concept of allometry can also be extended to predict PK parameters from adults to children. Three methods for extrapolating pediatric clearance were developed and evaluated using the clearance values of 4 drugs. The first method was established using a simple allometric (SA) model with estimated coefficient and exponent based on data ranging from children older than 2 years to adult. Then we developed a unified multistep single-exponent (MSE) and multistep body-weight-dependent exponent (MBDE) models. The major steps in these 2 new methods include generating pseudopredicted clearance for unobserved new populations such as preterm neonates, term neonates, and infants. Subsequent steps involve incorporating the pseudopredicted clearance with the actual PK data from older children and adults. All 3 models were then used to predict drug clearance in children ≤2 years old (N = 278). Drug clearance was predicted with mean absolute error of 29.6, 14.2, and 12.9 using SA, MSE, MBDE, respectively. The root mean square error was 65.9, 29.8, 24.7 for SA, MSE, MBDE, respectively. Approximately 41%, 72%, and 74% of the children's clearance data were within 0.5 to 1.5-fold of the observed values when drug clearance was extrapolated using SA, MSE, and MBDE models, respectively. The present multistep unified extrapolation approaches improved the prediction of clearance from preterm neonates to 2 years of age and may have practical use for first-in-pediatric dose selection.