Bundling arrows: improving translational CNS drug development by integrated PK/PD-metabolomics

Integration of tissue distribution, PK-PD modeling and metabolomics reveals inflammatory-immune response alterations in Gaultheria leucocarpa var. yunnanensis alleviating rheumatoid arthritis

ETHNOPHARMACOLOGICAL RELEVANCE

Gaultheria leucocarpa var. yunnanensis, a distinguished member of the Gaultheria Kalm ex L. in the Ericaceae family, has been traditionally employed in the southwestern regions of China for the efficacious treatment of rheumatoid arthritis (RA). The anti-RA fraction (ARF) derived from Gaultheria leucocarpa var. yunnanensis has been previously demonstrated to effectively alleviate RA in vivo and in vitro.

AIM OF THE STUDY

This research endeavor is dedicated to surveying the pharmacokinetic (PK) processes of ARF within plasma and tissues, profiling its metabolites in vivo, discerning the material foundation of its therapeutic efficacy, and delineating its anti-RA mechanisms.

MATERIALS AND METHODS

The prototype components and metabolites of ARF in plasma and seven tissues of RA rats were analyzed by LC-MSn. Advanced LC-MS/MS and HPLC-DAD methodologies were developed to investigate the plasma PK profiles and tissue distribution characteristics of MSTG-A, MSTG-B, and Gaultherin in both RA model rats and healthy controls. A panel of four cytokines (TNF-α, IL-1, IL-6, and IL-2) was selected as pharmacodynamic (PD) biomarkers and quantified using ELISA. The PK, PD, and PK-PD modeling of ARF were skillfully constructed by combining WinNonlin with Matlab software, enabling a comprehensive analysis of the interrelationships between components and effect markers. A non-targeted plasma metabolomics approach employing LC-QE-MS was utilized to insight into the underlying mechanisms of ARF alleviating RA.

RESULTS

The quantity and diversity of identified prototypical components and metabolites of ARF in model rat plasma increased over time. The spleen exhibited the highest number of metabolites and prototypical compounds of ARF. The UPLC-QQQ-MS/MS and HPLC-DAD method were developed and validated for the quantification of three chemical markers in rat plasma and tissues, respectively. Three effective components (MSTG-B, MSTG-A, and Gautherin) demonstrated linear dynamics in plasma and tissues at an oral dosage of 3 g/kg ARF. The PK-PD models involving three components and four inflammatory cytokines aligned with the one company model, demonstrating a linear correlation through compartmental modeling and curve fitting analysis. Significant variations were identified in the concentrations of various amino acids and lipid metabolites among the CON, ARF, and MTX groups in comparison to the MOD group, which are intricately linked to the inflammation-immunity response.

CONCLUSIONS

The three components displayed favorable bioavailability and were rapidly eliminated in RA rats, collectively exerting an anti-RA effect. The mechanism by which ARF mitigates RA is associated with the modulation of inflammation-immunity related metabolic pathways. The spleen may serve as the target tissue for ARF attenuating RA. These findings provide a robust foundation for rationalizing intervention strategies, elucidating biological mechanisms, and advancing the clinical application of ARF in the amelioration of RA.

Oxidative stress, dysfunctional energy metabolism, and destabilizing neurotransmitters altered the cerebral metabolic profile in a rat model of simulated heliox saturation diving to 4.0 MPa

The main objective of the present study was to determine metabolic profile changes in the brains of rats after simulated heliox saturated diving (HSD) to 400 meters of sea water compared to the blank controls. Alterations in the polar metabolome in the rat brain due to HSD were investigated in cortex, hippocampus, and striatum tissue samples by applying an NMR-based metabolomic approach coupled with biochemical detection in the cortex. The reduction in glutathione and taurine levels may hypothetically boost antioxidant defenses during saturation diving, which was also proven by the increased malondialdehyde level, the decreased superoxide dismutase, and the decreased glutathione peroxidase in the cortex. The concomitant decrease in aerobic metabolic pathways and anaerobic metabolic pathways comprised downregulated energy metabolism, which was also proven by the biochemical quantification of the metabolic enzymes Na-K ATPase and LDH in cerebral cortex tissue. The significant metabolic abnormalities of amino acid neurotransmitters, such as GABA, glycine, and aspartate, decreased aromatic amino acids, including tyrosine and phenylalanine, both of which are involved in the metabolism of dopamine and noradrenaline, which are downregulated in the cortex. Particularly, a decline in the level of N-acetyl aspartate is associated with neuronal damage. In summary, hyperbaric decompression of a 400 msw HSD affected the brain metabolome in a rat model, potentially including a broad range of disturbing amino acid homeostasis, metabolites related to oxidative stress and energy metabolism, and destabilizing neurotransmitter components. These disturbances may contribute to the neurochemical and neurological phenotypes of HSD.

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof‐of‐concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either “improvement,” defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or “no improvement,” defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree‐based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof‐of‐concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development.

Study on the therapeutic material basis and effect of Acanthopanax senticosus (Rupr. et Maxim.) Harms leaves in the treatment of ischemic stroke by PK-PD analysis based on online microdialysis-LC-MS/MS method

Leaves of Acanthopanax senticosus (Rupr. et Maxim.) Harms (ASL) have revealed significant biological activity in the treatment of ischemic stroke diseases. However, there was no in-depth study of the therapeutic material basis and effect of ASL from the pharmacokinetics-pharmacodynamics (PK-PD) analysis level. In this study, a method based on microdialysis coupled with ultra-performance liquid chromatography combined with triple quadruple mass spectrometry (MD-UPLC-QQQ-MS) was established to simultaneously and continuously collect and quantify the active compounds and endogenous neuroactive substances related to therapeutic effect in plasma and hippocampus of fully awake ischemic stroke rats. The acquired data were analyzed by the PK-PD analysis method. It was found that hyperoside, quercitrin, quercetin, and caffeic acid could pass through the blood-brain barrier, and quercetin needed a longer intake time than quercitrin and hyperoside, but the passage rate was higher. The exposure of the four compounds in the hippocampus affected the contents of seven neuroactive substances in different ways and was depicted graphically (concentration-time effect). In addition, the study found that the brain index and brain water content of ischemic stroke rats were significantly reduced after the oral administration of ASL. ASL observably regulated the content or activity of six important biochemical indexes in rats. On the one hand, this study verified that ASL could regulate ischemic stroke in many aspects. On the other hand, a visualized method to express the relationship between pharmacokinetics and pharmacodynamics in the hippocampus of cerebral ischemic areas was established. This research gives a hand to the study on the therapeutic material basis and effect of traditional Chinese medicine mechanism.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Development of a non-human primate model to support CNS translational research: Demonstration with D-amphetamine exposure and dopamine response

BACKGROUND

Challenges specific to the discovery and development of candidate CNS drugs have led to implementation of various in silico, in vitro and in vivo approaches to improve the odds for commercialization of novel treatments.

NEW METHOD

Advances in analytical methodology and microdialysis probe design have enabled development of a non-human primate model capable of measuring concentrations of drugs or endogenous chemicals in brain extracellular fluid (ECF) and cerebrospinal fluid (CSF). Linking these to population modeling reduces animal numbers to support predictive translational sciences in primates. Application to measure D-amphetamine exposure and dopamine response in ECF and CSF demonstrate the approach.

RESULTS

Following a 0.1 mg/kg intravenous bolus dose of D-amphetamine, a population approach was used to build a plasma compartmental-based and brain physiologic-based pharmacokinetic (PK) model linking drug concentrations in plasma to brain ECF and CSF concentrations. Dopamine was also measured in brain ECF. The PK model was used to simulate the relationship between D-amphetamine exposure and dopamine response in ECF over a wide dose range.

COMPARISONS WITH EXISTING METHODS

Ability to co-sample and measure drug and endogenous substances in blood, brain ECF and/or CSF, coupled with population modeling, provides an in vivo approach to evaluate CNS drug penetration and effect in non-human primates.

CONCLUSIONS

A method to measure drug and endogenous neurochemicals in non-human primate brain fluids is demonstrated. Its basis in non-human primates merits improved confidence regarding predictions of drug exposure and target engagement in human CNS.

Neuropharmacokinetics: a bridging tool between CNS drug development and therapeutic outcome

WHO classified neurological disorders to be among 6.3% of the global disease burden. Among the most central aspects of CNS drug development is the ability of novel molecules to cross the blood-brain barrier (BBB) to reach the target site over a desired time period for therapeutic action. Based on various aspects, brain pharmacokinetics is considered to be one of the foremost perspectives for the higher attrition rate of CNS biologics. Although drug traits are important, the BBB and blood-cerebrospinal fluid barrier together with transporters become the mechanistic approach behind CNS drug delivery. The present review emphasizes neuropharmacokinetic parameters, their importance, an assessment approach and the vast effect of transporters to brain drug distribution for CNS drug discovery.

Blood-Based Biomarkers of Quinpirole Pharmacology: Cluster-Based PK/PD and Metabolomics to Unravel the Underlying Dynamics in Rat Plasma and Brain

A key challenge in the development of central nervous system drugs is the availability of drug target specific blood-based biomarkers. As a new approach, we applied cluster-based pharmacokinetic/pharmacodynamic (PK/PD) analysis in brain extracellular fluid (brainECF ) and plasma simultaneously after 0, 0.17, and 0.86 mg/kg of the dopamine D2/3 agonist quinpirole (QP) in rats. We measured 76 biogenic amines in plasma and brainECF after single and 8-day administration, to be analyzed by cluster-based PK/PD analysis. Multiple concentration-effect relations were observed with potencies ranging from 0.001-383 nM. Many biomarker responses seem to distribute over the blood-brain barrier (BBB). Effects were observed for dopamine and glutamate signaling in brainECF , and branched-chain amino acid metabolism and immune signaling in plasma. Altogether, we showed for the first time how cluster-based PK/PD could describe a systems-response across plasma and brain, thereby identifying potential blood-based biomarkers. This concept is envisioned to provide an important connection between drug discovery and early drug development.