A minimal physiologically based pharmacokinetic model to study the combined effect of antibody size, charge, and binding affinity to FcRn/antigen on antibody pharmacokinetics.

Protein therapeutics have revolutionized the treatment of a wide range of diseases. While they have distinct physicochemical characteristics that influence their absorption, distribution, metabolism, and excretion (ADME) properties, the relationship between the physicochemical properties and PK is still largely unknown. In this work we present a minimal physiologically-based pharmacokinetic (mPBPK) model that incorporates a multivariate quantitative relation between a therapeutic's physicochemical parameters and its corresponding ADME properties. The model's compound-specific input includes molecular weight, molecular size (Stoke's radius), molecular charge, binding affinity to FcRn, and specific antigen affinity. Through derived and fitted empirical relationships, the model demonstrates the effect of these compound-specific properties on antibody disposition in both plasma and peripheral tissues using observed PK data in mice and humans. The mPBPK model applies the two-pore hypothesis to predict size-based clearance and exposure of full-length antibodies (150 kDa) and antibody fragments (50-100 kDa) within a onefold error. We quantitatively relate antibody charge and PK parameters like uptake rate, non-specific binding affinity, and volume of distribution to capture the relatively faster clearance of positively charged mAb as compared to negatively charged mAb. The model predicts the terminal plasma clearance of slightly positively and negatively charged antibody in humans within a onefold error. The mPBPK model presented in this work can be used to predict the target-mediated disposition of a drug when compound-specific and target-specific properties are known. To our knowledge, a combined effect of antibody weight, size, charge, FcRn, and antigen has not been incorporated and studied in a single mPBPK model previously. By conclusively incorporating and relating a multitude of protein's physicochemical properties to observed PK, our mPBPK model aims to contribute as a platform approach in the early stages of drug development where many of these properties can be optimized to improve a molecule's PK and ultimately its efficacy.

Printed Stretchable Graphene Conductors for Wearable Technology.

Skin-compatible printed stretchable conductors that combine a low gauge factor with a high durability over many strain cycles are still a great challenge. Here, a graphene nanoplatelet-based colloidal ink utilizing a skin-compatible thermoplastic polyurethane (TPU) binder with adjustable rheology is developed. Stretchable conductors that remain conductive even under 100% strain and demonstrate high fatigue resistance to cyclic strains of 20-50% are realized via printing on TPU. The sheet resistances of these conductors after drying at 120 °C are as low as 34 Ω â–¡-1 mil-1. Furthermore, photonic annealing at several energy levels is used to decrease the sheet resistance to <10 Ω â–¡-1 mil-1, with stretchability and fatigue resistance being preserved and tunable. The high conductivity, stretchability, and cyclic stability of printed tracks having excellent feature definition in combination with scalable ink production and adjustable rheology bring the high-volume manufacturing of stretchable wearables into scope.

A multi-model approach to predict efficacious clinical dose for an anti-TGF-ß antibody (GC2008) in the treatment of osteogenesis imperfecta.

Osteogenesis imperfecta (OI) is a heterogeneous group of inherited bone dysplasias characterized by reduced skeletal mass and bone fragility. Although the primary manifestation of the disease involves the skeleton, OI is a generalized connective tissue disorder that requires a multidisciplinary treatment approach. Recent studies indicate that application of a transforming growth factor beta (TGF-ß) neutralizing antibody increased bone volume fraction (BVF) and strength in an OI mouse model and improved bone mineral density (BMD) in a small cohort of patients with OI. In this work, we have developed a multitiered quantitative pharmacology approach to predict human efficacious dose of a new anti-TGF-ß antibody drug candidate (GC2008). This method aims to translate GC2008 pharmacokinetic/pharmacodynamic (PK/PD) relationship in patients, using a number of appropriate mathematical models and available preclinical and clinical data. Compartmental PK linked with an indirect PD effect model was used to characterize both pre-clinical and clinical PK/PD data and predict a GC2008 dose that would significantly increase BMD or BVF in patients with OI. Furthermore, a physiologically-based pharmacokinetic model incorporating GC2008 and the body's physiological properties was developed and used to predict a GC2008 dose that would decrease the TGF-ß level in bone to that of healthy individuals. By using multiple models, we aim to reveal information for different aspects of OI disease that will ultimately lead to a more informed dose projection of GC2008 in humans. The different modeling efforts predicted a similar range of pharmacologically relevant doses in patients with OI providing an informed approach for an early clinical dose setting.

Machine Learning guided early drug discovery of small molecules.

Machine learning (ML) approaches have been widely adopted within the early stages of the drug discovery process, particularly within the context of small-molecule drug candidates. Despite this, the use of ML is still limited in the pharmacokinetic/pharmacodynamic (PK/PD) application space. Here, we describe recent progress and the role of ML used in preclinical drug discovery. We summarize the advances and current strategies used to predict ADME (absorption, distribution, metabolism and, excretion) properties of small molecules based on their structures, and predict structures based on the desired properties for molecular screening and optimization. Finally, we discuss the use of ML to predict PK to rank the ability of drug candidates to achieve appropriate exposures and hence provide important insights into safety and efficacy.

Development of a genetic algorithm and NONMEM workbench for automating and improving population pharmacokinetic/pharmacodynamic model selection.

The current approach to selection of a population PK/PD model is inherently flawed as it fails to account for interactions between structural, covariate, and statistical parameters. Further, the current approach requires significant manual and redundant model modifications that heavily lend themselves to automation. Within the discipline of numerical optimization it falls into the "local search" category. Genetic algorithms are a class of algorithms inspired by the mathematics of evolution. GAs are general, powerful, robust algorithms and can be used to find global optimal solutions for difficult problems even in the presence of non-differentiable functions, as is the case in the discrete nature of including/excluding model components in search of the best performing mixed-effects PK/PD model. A genetic algorithm implemented in an R-based NONMEM workbench for identification of near optimal models is presented. In addition to the GA capabilities, the workbench supports modeling efforts by: (1) Organizing and displaying models in tabular format, allowing the user to sort, filter, edit, create, and delete models seamlessly, (2) displaying run results, parameter estimates and precisions, (3) integrating xpose4 and PsN to facilitate generation of model diagnostic plots and run PsN scripts, (4) running regression models between post-hoc parameter estimates and covariates. This approach will further facilitate the scientist to shift efforts to focus on model evaluation, hypotheses generation, and interpretation and applications of resulting models.

Model-Guided Antipsychotic Dose Reduction in Schizophrenia: A Pilot, Single-Blind Randomized Controlled Trial.

PURPOSE/BACKGROUND: Patients with schizophrenia as well as their psychiatrists are hesitant to reduce the antipsychotic dose in fear of relapse. To overcome such dilemmas, we developed models to individually calculate an oral dose that corresponds to a given target dopamine D2 receptor occupancy. METHODS/PROCEDURES: In this pilot, 52-week single-blind randomized controlled trial, 35 clinically stable patients with schizophrenia receiving either risperidone or olanzapine monotherapy were randomly assigned to dose reduction (n = 17) or dose maintenance group (n = 18). In the former group, baseline doses were reduced to the doses corresponding to 65% D2 occupancy (the lower end of therapeutic window) at trough that were calculated from randomly collected plasma concentrations using our models. FINDINGS/RESULTS: In the dose reduction group, doses of risperidone and olanzapine were decreased from 4.2 ± 1.9 to 1.4 ± 0.4 and 12.8 ± 3.9 to 6.7 ± 1.8 mg/d, whereas the doses in the dose maintenance group were 4.3 ± 1.9 and 15.8 ± 4.6 mg/d, respectively. Twelve subjects (70.5%) and 13 subjects (72.2%) in the dose reduction and dose maintenance groups completed the study (P = 0.604), whereas 3 subjects (18.8%) and none dropped out because of clinical worsening in the dose reduction and dose maintenance groups, respectively. There were not significant differences in score changes in Positive and Negative Syndrome Scale between the 2 groups but in Positive subscale scores in the Clinical Global Impression-Schizophrenia (0.4 ± 0.7 in the dose reduction group vs -0.1 ± 0.7 in the dose maintenance group, P = 0.029). IMPLICATIONS/CONCLUSIONS: Although our model-guided dose reduction strategy was found to be comparable with no-dose change in terms of dropout rates, safety issues have to be further examined.

Circadian patterns of hallucinatory experiences in patients with schizophrenia: Potentials for chrono-pharmacology.

The objective of this study was to investigate possible circadian pattern of psychotic symptoms in patients with schizophrenia, which could be reflected on the dosing schedule/regimen, i.e. chrono-pharmacology. Patients with schizophrenia (ICD-10) who reported having auditory hallucination, receiving monotherapy with risperidone, olanzapine or paliperidone for at least two weeks were included. The subjects were provided a diary and asked to record the time and duration of auditory hallucinations during the eight time periods (i.e. 00:00-03:00, 03:00-06:00, 06:00-09:00, 09:00-12:00, 12:00-15:00, 15:00-18:00, 18:00-21:00, and 21:00-24:00). In the diary, times of medication doses and sleep were also recorded. Time and degree of peak and trough dopamine D2 receptor blockade with antipsychotics were estimated from 2 sparsely collected plasma drug concentrations. The prevalence and duration of auditory hallucinations were statistically examined among the eight time periods, respectively. Forty-nine patients participated in this study (mean ±â€¯SD age, 50.7 ±â€¯14.8 years; 36 men (73.5%); 34 inpatients (69.4%)). Auditory hallucinations occurred most frequently and lasted for the longest duration in the period of 18:00-21:00 (75.5% (37/49) and 1.37 ±â€¯1.67 h). This happened despite the fact that the difference in D2 receptor occupancy between the peak and trough was less than 2%, indicating a stable drug delivery. Since the dopamine D2 receptor blockade by antipsychotics was stable, the nocturnal circadian pattern found in this study may reflect intrinsic dopaminergic fluctuation or generally quieter environments at night. These circadian patterns may be considered to devise individualized treatment approach in the context of "chrono-pharmacology" for patients with schizophrenia.

Estimating parameters of nonlinear dynamic systems in pharmacology using chaos synchronization and grid search.

Bridging fundamental approaches to model optimization for pharmacometricians, systems pharmacologists and statisticians is a critical issue. These fields rely primarily on Maximum Likelihood and Extended Least Squares metrics with iterative estimation of parameters. Our research combines adaptive chaos synchronization and grid search to estimate physiological and pharmacological systems with emergent properties by exploring deterministic methods that are more appropriate and have potentially superior performance than classical numerical approaches, which minimize the sum of squares or maximize the likelihood. We illustrate these issues with an established model of cortisol in human with nonlinear dynamics. The model describes cortisol kinetics over time, including its chaotic oscillations, by a delay differential equation. We demonstrate that chaos synchronization helps to avoid the tendency of the gradient-based optimization algorithms to end up in a local minimum. The subsequent analysis illustrates that the hybrid adaptive chaos synchronization for estimation of linear parameters with coarse-to-fine grid search for optimal values of non-linear parameters can be applied iteratively to accurately estimate parameters and effectively track trajectories for a wide class of noisy chaotic systems.

Chaos synchronization and Nelder-Mead search for parameter estimation in nonlinear pharmacological systems: Estimating tumor antigenicity in a model of immunotherapy.

In mathematical pharmacology, models are constructed to confer a robust method for optimizing treatment. The predictive capability of pharmacological models depends heavily on the ability to track the system and to accurately determine parameters with reference to the sensitivity in projected outcomes. To closely track chaotic systems, one may choose to apply chaos synchronization. An advantageous byproduct of this methodology is the ability to quantify model parameters. In this paper, we illustrate the use of chaos synchronization combined with Nelder-Mead search to estimate parameters of the well-known Kirschner-Panetta model of IL-2 immunotherapy from noisy data. Chaos synchronization with Nelder-Mead search is shown to provide more accurate and reliable estimates than Nelder-Mead search based on an extended least squares (ELS) objective function. Our results underline the strength of this approach to parameter estimation and provide a broader framework of parameter identification for nonlinear models in pharmacology.