A quantitative systems pharmacology study on optimal scenarios for switching to paliperidone palmitate once-monthly

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.

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

Effects of long-acting injectable antipsychotics versus oral antipsychotics on autonomic nervous system activity in schizophrenic patients

OBJECTIVE

Long-acting injections (LAIs) of antipsychotics show distinct pharmacokinetic profiles from oral antipsychotics (OAPs). Although there may be differences in adverse event frequency, any differences in their effects on autonomic nervous system (ANS) remain unclear.

PATIENTS AND METHODS

In total, 270 schizophrenic patients were recruited in this study: 241 received OAPs (risperidone, olanzapine, quetiapine, or aripiprazole) and 29 received LAIs (risperidone LAI, aripiprazole LAI, or paliperidone palmitate) as monotherapy. Heart rate variability was measured as an index of ANS activity, and the low-frequency (0.03-0.15 Hz) component, high-frequency (0.15-0.40 Hz) component, and total power (0.03-0.40 Hz) were calculated. Components were compared between the groups using t-tests.

RESULTS

A significant difference was detected in the low-frequency component between the OAP and LAI groups (P=0.046). No significant difference was found in total power or the high-frequency component between the two groups.

CONCLUSION

Compared with OAPs, LAIs have fewer adverse effects on ANS activity, particularly the low-frequency component, as determined using a spectral analysis of heart rate variability.