Tools for predicting the PK/PD of therapeutic proteins

Towards a comprehensive assessment of QSP models: what would it take?

Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

The New Era of Drug Discovery: The Power of Computer-aided Drug Design (CADD)

Abstract:

Drug design and discovery is a process that requires high financial costs and is timeconsuming. For many years, this process focused on empirical pharmacology. However, over the years, the target-based approach allowed a significant discovery in this field, initiating the rational design era. In view, to decrease the time and financial cost, rational drug design is benefited by increasing computer engineering and software development, and computer-aided drug design (CADD) emerges as a promising alternative. Since the 1970s, this approach has been able to identify many important and revolutionary compounds, like protease inhibitors, antibiotics, and others. Many anticancer compounds identified through this approach have shown their importance, being CADD essential in any drug discovery campaign. Thus, this perspective will present the prominent successful cases utilizing this approach and entering into the next stage of drug design. We believe that drug discovery will follow the progress in bioinformatics, using high-performance computing with molecular dynamics protocols faster and more effectively. In addition, artificial intelligence and machine learning will be the next process in the rational design of new drugs. Here, we hope that this paper generates new ideas and instigates research groups worldwide to use these methods and stimulate progress in drug design.

Application of physiologically-based pharmacokinetic models for therapeutic proteins and other novel modalities

The past two decades have seen diversification of drug development pipelines and approvals from traditional small molecule therapies to alternative modalities including monoclonal antibodies, engineered proteins, antibody drug conjugates (ADCs), oligonucleotides and gene therapies. At the same time, physiologically-based pharmacokinetic (PBPK) models for small molecules have seen increased industry and regulatory acceptance.This review focusses on the current status of the application of PBPK models to these newer modalities and give a perspective on the successes, challenges and future directions of this field.There is greatest experience in the development of PBPK models for therapeutic proteins, and PBPK models for ADCs benefit from prior experience for both therapeutic proteins and small molecules. For other modalities, the application of PBPK models is in its infancy.Challenges are discussed and a common theme is lack of availability of physiological and experimental data to characterise systems and drug parameters to enable a priori prediction of pharmacokinetics. Furthermore, sufficient clinical data are required to build confidence in developed models.The PBPK modelling approach provides a quantitative framework for integrating knowledge and data from multiple sources and can be built on as more data becomes available.

Translating pharmacology models effectively to predict therapeutic benefit

Many in vitro and in vivo models are used in pharmacological research to evaluate the role of targeted proteins in a disease. Understanding the translational relevance and limitation of these models for analyzing the disposition, pharmacokinetic/pharmacodynamic (PK/PD) profile, mechanism, and efficacy of a drug, is essential when selecting the most appropriate model of the disease of interest and predicting clinically efficacious doses of the investigational drug. Here, we review selected animal models used in ophthalmology, infectious diseases, oncology, autoimmune diseases, and neuroscience. Each area has specific challenges around translatability and determination of an efficacious dose: new patient-specific dosing methods could help overcome these limitations.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

Nuciferine, an active ingredient derived from lotus leaf, lights up the way for the potential treatment of obesity and obesity-related diseases

Obesity, is an increasingly global public health problem associated complications. However, the proven anti-obesity agents are inefficient with adverse side effects; hence attention is being paid to novel drugs from natural resources to manage obesity and obesity-related diseases. Nuciferine (NF) is a high-quality aporphine alkaloid present in lotus leaf. Unlike the chemical drugs, NF elicits anti-obesity, anti-dyslipidemia, anti-hyperglycemic, anti-hypouricemic, anti-inflammatory, and anti-tumor effects, and affinity to neural receptors, and protection against obesity-related diseases. The underlying mechanism of NF includes the regulation of targeted molecules and pathways related to metabolism, inflammation, and cancer and modulation of Ca²⁺ flux, gut microbiota, and ferroptosis. Besides, the clinical application, availability, pharmacokinetics, pharmaceutics, and security of NF have been established, highlighting the potential of developing NF as an anti-obesity agent. Therefore, this review provides a comprehensive summarization, which sheds light on future research in NF.

Plasma and intracerebral pharmacokinetics and pharmacodynamics modeling for the acetylcholine releasing effect of ginsenoside Rg1 in mPFC of Aβ model rats

Ginsenoside Rg1 is a major bioactive component of ginseng. Limited information is available regarding Rg1 concentrations in the central neural system and the corresponding relationship of plasma/intracerebral concentrations, and intracerebral effects of Rg1. Awake Aβ model rats received a single subcutaneous administration of Rg1. Concentrations of unbound Rg1 and acetylcholine in the brain extracellular fluid and Rg1 in plasma were then determined. An E_max-two compartment pharmacokinetic/pharmacodynamics (PK/PD) model without effect compartment was finally obtained by evaluating three mechanism-based models. The corresponding relationship between the plasma PK and PD of Rg1 can be described as E = 119.05•C/(73.42 + C).[Formula: see text].

Allometric scaling of therapeutic monoclonal antibodies in preclinical and clinical settings

Constant technological advancement enabled the production of therapeutic monoclonal antibodies (mAbs) and will continue to contribute to their rapid expansion. Compared to small-molecule drugs, mAbs have favorable characteristics, but also more complex pharmacokinetics (PK), e.g., target-mediated nonlinear elimination and recycling by neonatal Fc-receptor. This review briefly discusses mAb biology, similarities and differences in PK processes across species and within human, and provides a detailed overview of allometric scaling approaches for translating mAb PK from preclinical species to human and extrapolating from adults to children. The approaches described here will remain vital in mAb drug development, although more data are needed, for example, from very young patients and mAbs with nonlinear PK, to allow for more confident conclusions and contribute to further growth of this field. Improving mAb PK predictions will facilitate better planning of (pediatric) clinical studies and enable progression toward the ultimate goal of expediting drug development.

Informing Development of Bispecific Antibodies Using Physiologically Based Pharmacokinetic-Pharmacodynamic Models: Current Capabilities and Future Opportunities

Antibody therapeutics continue to represent a significant portion of the biotherapeutic pipeline, with growing promise for bispecific antibodies (BsAbs). BsAbs can target 2 different antigens at the same time, such as simultaneously binding tumor-cell receptors and recruiting cytotoxic immune cells. This simultaneous engagement of 2 targets can be potentially advantageous, as it may overcome disadvantages posed by a monotherapy approach, like the development of resistance to treatment. Combination therapy approaches that modulate 2 targets simultaneously offer similar advantages, but BsAbs are more efficient to develop. Unlike combination approaches, BsAbs can facilitate spatial proximity of targets that may be necessary to induce the desired effect. Successful development of BsAbs requires understanding antibody formatting and optimizing activity for both targets prior to clinical trials. To realize maximal efficacy, special attention is required to fully define pharmacokinetic (PK)/pharmacodynamic (PD) relationships enabling selection of dose and regimen. The application of physiologically based pharmacokinetics (PBPK) has been evolving to inform the development of novel treatment modalities such as bispecifics owing to the increase in our understanding of pharmacology, utility of multiscale models, and emerging clinical data. In this review, we discuss components of PBPK models to describe the PK characteristics of BsAbs and expand the discussion to integration of PBPK and PD models to inform development of BsAbs. A framework that can be adopted to build PBPK-PD models to inform the development of BsAbs is also proposed. We conclude with examples that highlight the application of PBPK-PD and share perspectives on future opportunities for this emerging quantitative tool.

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.

A systems pharmacokinetic/pharmacodynamic model for concizumab to explore the potential of anti-TFPI recycling antibodies

Concizumab is a humanized monoclonal antibody in clinical investigation directed against membrane-bound and soluble tissue factor pathway inhibitor (mTFPI and sTFPI) for treatment of hemophilia. Concizumab displays a non-linear pharmacokinetic (PK) profile due to mTFPI-mediated endocytosis and necessitates a high dose and frequent dosing to suppress the abundant sTFPI, a negative regulator of coagulation. Recycling antibodies that can dissociate bound mTFPI/sTFPI in endosomes for degradation and rescue antibody from degradation have a potential in reducing the dose by extending antibody systemic persistence and sTFPI suppression. We developed a systems PK/pharmacodynamics (PD) model with nested endosome compartments to simulate the effect of decreased antibody binding to mTFPI/sTFPI in endosomes on antibody clearance and sTFPI suppression for exploring the potential of anti-TFPI recycling antibodies in reducing the dose. A dynamic model-building strategy was taken. A reduced PK/PD model without the endosome compartments was developed to optimize unknown target turnover parameters using concizumab PK data. The optimized parameters were then employed in the systems PK/PD model for simulations. The obtained systems PK/PD model adequately described the PK of concizumab in rabbits, monkeys, and humans and the PD in humans. The systems PK/PD model predicted that an anti-TFPI recycling antibody with a 100-fold higher mTFPI/sTFPI dissociation constant in endosomes than concizumab can extend sTFPI suppression from 12 days to 1 month. Thus, the systems PK/PD model provides a quantitative platform for guiding the engineering and translational development of anti-TFPI recycling antibodies.

Human FcRn Tissue Expression Profile and Half-Life in PBMCs

System-wide quantitative characterization of human neonatal Fc receptor (FcRn) properties is critical for understanding and predicting human PK (pharmacokinetics) as well as the distribution of mAbs and Fc-fusion proteins using PBPK (physiologically-based pharmacokinetic) modeling. To this end, tissue-specific FcRn expression and half-life are important model inputs. Herein, human FcRn tissue expression was measured by peptide immunoaffinity chromatography coupled with high-resolution mass spectrometry. FcRn concentrations across 14 human tissues ranged from low to 230 pmol per gram of tissue. Furthermore, the FcRn half-life was determined to be 11.1 h from a human stable isotope labelled leucine pulse labeling experiment. The spatial and temporal quantitative human FcRn data now promise to enable a refined PBPK model with improved accuracy of human PK predictions for Fc-containing biotherapeutics.

Bioanalytical challenges and unique considerations to support pharmacokinetic characterization of bispecific biotherapeutics

Compared with conventional (monospecific) therapeutics, bispecific protein therapeutics present unique challenges for pharmacokinetic (PK) characterization – namely, the characterization of multiple functional domains as well as the consideration of biotransformation or interference by the formation of antitherapeutic antibodies against each functional domain. PK characterization is essential to the success of the overall drug development plan and for molecules with multiple binding domains; multiple bioanalytical methods may be needed to answer critical questions for each phase of drug development. The number of bispecific protein therapeutics entering drug development continues to increase, and therefore, a bioanalytical strategy for the PK characterization of bispecific molecules and study of their in vivo structure–function relationship is needed. This review presents case studies and a regulatory perspective.

Quantification of IgG monoclonal antibody clearance in tissues

Monoclonal antibodies are an important therapeutic entity, and knowledge of antibody pharmacokinetics has steadily increased over the years. Despite this effort, little is known about the extent of IgG antibody degradation in different tissues of the body. While studies have been published identifying sites of degradation with the use of residualizing and non-residualizing radiolabels, quantitative tissue clearances have not yet been derived. Here, we show that in physiologically-based pharmacokinetic (PBPK) models we can combine mouse data of Indium-111 and Iodine-125 labeled antibodies with prior physiologic knowledge to determine tissue-specific intrinsic clearances. Unspecific total tissue clearance (mL/day) in the mouse was estimated to be: liver = 4.75; brain = 0.02; gut = 0.40; heart = 0.07; kidney = 0.97; lung = 0.20; muscle = 3.02; skin = 3.89; spleen = 0.45; rest of body = 2.16. The highest catabolic activity (per g tissue) was in spleen for an FcRn wild-type antibody, but shifts to the liver for an antibody with reduced FcRn affinity. In the model developed, this shift can be explained by the liver having a greater FcRn-mediated protection capacity than the spleen. The quantification of tissue intrinsic clearances and FcRn salvage capacity increases our understanding of quantitative processes that drive the therapeutic responses of antibodies. This knowledge is critical, for instance to estimate the non-specific cellular uptake and degradation of antibodies used for targeted delivery of payloads.

Pharmacokinetic, pharmacodynamic and biodistribution following oral administration of nanocarriers containing peptide and protein drugs

The influence of nanoparticle (NP) formulations on the pharmacokinetic, pharmacodynamic and biodistribution profiles of peptide- and protein-like drugs following oral administration is critically reviewed. The possible mechanisms of absorption enhancement and the effects of the physicochemical properties of the NP are examined. The potential advantages and challenges of physiologically-based pharmacokinetic (PBPK) modelling to help predict efficacy in man are discussed. The importance of developing and expanding the regulatory framework to help translate the technology into the clinic and accelerate the availability of oral nanoparticulate formulations is emphasized. In conclusion, opportunities for future work to improve the state of the art of oral nanomedicines are identified.

Deciphering the In Vivo Performance of a Monoclonal Antibody to Neutralize Its Soluble Target at the Site of Action in a Mouse Collagen-Induced Arthritis Model

PURPOSE

Study the disposition and target-neutralization capability of an anti-interleukin-6 (IL-6) monoclonal antibody (mAb) at the joint in a mouse collagen-induced arthritis (CIA) model.

METHODS

A mechanistic pharmacokinetic/pharmacodynamic study was conducted in a mouse CIA model using CNTO 345, a rat anti-mouse IL-6 mAb, as model compound. The drug, total/free IL-6 concentrations in both serum and joint lavage fluid were quantitatively assessed and compared to those in the normal control mice.

RESULTS

CNTO 345 exhibited higher clearance and significantly higher joint lavage/serum ratio in the CIA mice than in the normal control mice. The mAb concentrations in the joint lavage are approximately proportional to the serum concentrations at all the time points being examined. Dosing of CNTO 345 led to sustained free IL-6 suppression in both serum and joint lavage in a dose-dependent manner. A dose-dependent increase in total IL-6 was observed in serum, but not in the joint lavage fluid. Though no change in disease activity was observed following a single dose of anti-IL-6 mAb at peak of the disease, a dose-dependent decrease in serum amyloid A, a downstream biomarker of IL-6, was observed.

CONCLUSIONS

This study provided quantitative assessments of the distribution and target-neutralization capability of an anti-IL-6 mAb at the site of action in an animal disease model.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.