Using Simcyp to project human oral pharmacokinetic variability in early drug research to mitigate mechanism-based adverse events

Clinical Pharmacology in Drug Development for Rare Diseases in Neurology: Contributions and Opportunities

Several challenges are associated with rare disease drug development in neurology. In this article, we summarize the US Food and Drug Administration's experience with clinical drug development for rare neurological diseases and discuss clinical pharmacology's critical contributions to drug development for rare diseases. We used publicly available information to identify and screen drug products approved for rare neurological indications between 1983 and 2019. We highlighted cases in which clinical pharmacology contributed to the evidence of drug efficacy, dose selection for pivotal clinical trials, dose optimization based on intrinsic and extrinsic factors, pharmacokinetic bridging for formulations, and efficacy bridging across different racial groups. Fifty-one approved drug products were identified since the introduction of the Orphan Drug Act in 1983. Interestingly, the number of approvals in the last few years increased significantly, probably due to advances in genomic research and targeted drug modalities. Evaluation of dose selection in patient populations showed that in 52% of cases, the sponsors did not evaluate efficacy for more than one or two dose levels throughout the development program. Clinical pharmacology studies to evaluate the effect of intrinsic or extrinsic factors were adequately characterized in most of the applications. With the expansion of model informed drug development applications, (e.g., quantitative systems pharmacology and deep learning neural network models), the role and impact of clinical pharmacology is expected to grow exponentially in the next decade and enhance the development of novel treatment modalities for neurological rare diseases.

Prediction of active human dose: learnings from 20 years of Merck KGaA experience, illustrated by case studies

High-quality dose predictions based on a good understanding of target engagement is one of the main translational goals in drug development. Here, we systematically evaluate active human dose predictions for 15 Merck KGaA/EMD Serono assets spanning several modalities and therapeutic areas. Using case studies, we illustrate the value of adhering to the translational best practices of having an exposure-response relationship in an appropriate animal model; having validated, translatable pharmacodynamic (PD) biomarkers measurable in Phase I populations in the right tissue; having a deeper understanding of biology; and capturing uncertainties in predictions. Given the gap in publications on the subject, we believe that the learnings from this unique diverse data set, which are generic to the industry, will trigger actions to improve future predictions.

Cryopreserved Human Intestinal Mucosal Epithelium: A Novel In Vitro Experimental System for the Evaluation of Enteric Drug Metabolism, Cytochrome P450 Induction, and Enterotoxicity

We report here a novel in vitro enteric experimental system, cryopreserved human intestinal mucosa (CHIM), for the evaluation of enteric drug metabolism, drug-drug interaction, drug toxicity, and pharmacology. CHIM was isolated from the small intestines of four human donors. The small intestines were first dissected into the duodenum, jejunum, and ileum, followed by collagenase digestion of the intestinal lumen. The isolated mucosa was gently homogenized to yield multiple cellular fragments, which were then cryopreserved in a programmable liquid cell freezer and stored in liquid nitrogen. After thawing and recovery, CHIM retained robust cytochrome P450 (P450) and non-P450 drug-metabolizing enzyme activities and demonstrated dose-dependent induction of transcription of CYP24A1 (approximately 300-fold) and CYP3A4 (approximately 3-fold) by vitamin D₃ as well as induction of CYP3A4 (approximately 3-fold) by rifampin after 24 hours of treatment. Dose-dependent decreases in cell viability quantified by cellular ATP content were observed for naproxen and acetaminophen, with higher enterotoxicity observed for naproxen, consistent with that observed in humans in vivo. These results suggest that CHIM may be a useful in vitro experimental model for the evaluation of enteric drug properties, including drug metabolism, drug-drug interactions, and drug toxicity.

Are Physiologically Based Pharmacokinetic Models Reporting the Right C(max)? Central Venous Versus Peripheral Sampling Site

Physiologically based pharmacokinetic (PBPK) models can over-predict maximum plasma concentrations (C_max) following intravenous administration. A proposed explanation is that invariably PBPK models report the concentration in the central venous compartment, rather than the site where the samples are drawn. The purpose of this study was to identify and validate potential corrective models based on anatomy and physiology governing the blood supply at the site of sampling and incorporate them into a PBPK platform. Four models were developed and scrutinised for their corrective potential. All assumed the peripheral sampling site concentration could be described by contributions from surrounding tissues and utilised tissue-specific concentration-time profiles reported from the full-PBPK model within the Simcyp Simulator. Predicted concentrations for the peripheral site were compared to the observed C_max. The models results were compared to clinical data for 15 studies over seven compounds (alprazolam, imipramine, metoprolol, midazolam, omeprazole, rosiglitazone and theophylline). The final model utilised tissue concentrations from adipose, skin, muscle and a contribution from artery. Predicted C_max values considering the central venous compartment can over-predict the observed values up to 10-fold whereas the new sampling site predictions were within 2-fold of observed values. The model was particularly relevant for studies where traditional PBPK models over-predict early time point concentrations. A successful corrective model for C_max prediction has been developed, subject to further validation. These models can be enrolled as built-up modules into PBPK platforms and potentially account for factors that may affect the initial mixing of the blood at the site of sampling.

The discovery and characterization of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor potentiator N-{(3S,4S)-4-[4-(5-cyano-2-thienyl)phenoxy]tetrahydrofuran-3-yl}propane-2-sulfonamide (PF-04958242)

A unique tetrahydrofuran ether class of highly potent α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor potentiators has been identified using rational and structure-based drug design. An acyclic lead compound, containing an ether-linked isopropylsulfonamide and biphenyl group, was pharmacologically augmented by converting it to a conformationally constrained tetrahydrofuran to improve key interactions with the human GluA2 ligand-binding domain. Subsequent replacement of the distal phenyl motif with 2-cyanothiophene to enhance its potency, selectivity, and metabolic stability afforded N-{(3S,4S)-4-[4-(5-cyano-2-thienyl)phenoxy]tetrahydrofuran-3-yl}propane-2-sulfonamide (PF-04958242, 3), whose preclinical characterization suggests an adequate therapeutic index, aided by low projected human oral pharmacokinetic variability, for clinical studies exploring its ability to attenuate cognitive deficits in patients with schizophrenia.

Harnessing pharmacological knowledge for personalized medicine and pharmacotyping: Challenges and lessons learned

The contribution of the genetic make-up to an individual’s capacity has long been recognized in modern pharmacology as a crucial factor leading to therapy inefficiency and toxicity, negatively impacting the economic burden of healthcare and restricting the monitoring of diseases. In practical terms, and in order for drug prescription to be improved toward meeting the personalized medicine concept in drug delivery, the maximum clinical outcome for most, if not all, patients must be achieved, i.e., pharmacotyping. Such a direction although promising and of high expectation from the society, it is however hardly to be afforded for healthcare worldwide. To overcome any existed hurdles, this means that practical clinical utility of personalized medicine decisions have to be documented and validated in the clinical setting. The latter implies for drug delivery the efficient implementation of previously gained in vivo pharmacology experience with pharmacogenomics knowledge. As an approach to work faster and in a more productive way, the elaboration of advanced physiologically based pharmacokinetics models is discussed. And in better clarifying this topic, the example of tamoxifen is thoroughly presented. Overall, pharmacotyping represents a major challenge in modern therapeutics for which pharmacologists need to work in successfully fulfilling this task.

Predicting drug-drug interactions: application of physiologically based pharmacokinetic models under a systems biology approach

The development of in vitro-in vivo extrapolation (IVIVE), a 'bottom-up' approach, to predict pharmacokinetic parameters and drug-drug interactions (DDIs) has accelerated mainly due to an increase in the understanding of the multiple mechanisms involved in these interactions and the availability of appropriate in vitro systems that act as surrogates for delineating various elements of the interactions relevant to absorption, distribution, metabolism and elimination. Recent advances in the knowledge of the population variables required for IVIVE (demographic, anatomical, genetic and physiological parameters) have also contributed to the appreciation of the sources of variability and wider use of this approach for different scenarios within the pharmaceutical industry. Initially, the authors present an overview of the integration of IVIVE into 'static' and 'dynamic' models for the quantitative prediction of DDIs. The main purpose of this review is to discuss the application of IVIVE in conjunction with physiologically based pharmacokinetic modeling under a systems biology approach to characterize the potential DDIs in individual patients, including those who cannot be investigated in formal clinical trials for ethical reasons. In addition, we address the issues related to the prediction of complex DDIs involving the inhibition of cytochrome P- and transporter-mediated activities through multiple drugs.

Positive allosteric modulation of AMPA receptors from efficacy to toxicity: the interspecies exposure-response continuum of the novel potentiator PF-4778574

α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) positive allosteric modulation (i.e., "potentiation") has been proposed to overcome cognitive impairments in schizophrenia, but AMPAR overstimulation can be excitotoxic. Thus, it is critical to define carefully a potentiator's mechanism-based therapeutic index (TI) and to determine confidently its translatability from rodents to higher-order species. Accordingly, the novel AMPAR potentiator N-{(3R,4S)-3-[4-(5-cyano-2-thienyl)phenyl]tetrahydro-2H-pyran-4-yl}propane-2-sulfonamide (PF-4778574) was characterized in a series of in vitro assays and single-dose animal studies evaluating AMPAR-mediated activities related to cognition and safety to afford an unbound brain compound concentration (Cb,u)-normalized interspecies exposure-response relationship. Because it is unknown which AMPAR subtype(s) may be selectively potentiated for an optimal TI, PF-4778574 binding affinity and functional potency were determined in rodent tissues expected to express a native mixture of AMPAR subunits and their associated proteins to afford composite pharmacological values. Functional activity was also quantified in recombinant cell lines stably expressing human GluA2 flip or flop homotetramers. Procognitive effects of PF-4778574 were evaluated in both rat electrophysiological and nonhuman primate (nhp) behavioral models of pharmacologically induced N-methyl-d-aspartate receptor hypofunction. Safety studies assessed cerebellum-based AMPAR activation (mouse) and motor coordination disruptions (mouse, dog, and nhp), as well as convulsion (mouse, rat, and dog). The resulting empirically derived exposure-response continuum for PF-4778574 defines a single-dose-based TI of 8- to 16-fold for self-limiting tremor, a readily monitorable clinical adverse event. Importantly, the Cb,u mediating each physiological effect were highly consistent across species, with efficacy and convulsion occurring at just fractions of the in vitro-derived pharmacological values.

Case studies for practical food effect assessments across BCS/BDDCS class compounds using in silico, in vitro, and preclinical in vivo data

Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration-time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1-4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound's BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions.