Modelli Computazionali nello studio di farmaci per patologie del Sistema Nervoso Centrale

[Geerts H, Spiros A, Roberts P, Carr R. Has the time come for predictive computer modeling in CNS drug discovery and development? CPT Pharmacometrics Syst Pharmacol. 2012 Nov 28;1:e16. doi: 10.1038/psp.2012.17.]

Full Text: http://www.nature.com/psp/journal/v1/n11/full/psp201217a.html

Abstract:

Discutiamo se un nuovo paradigma, la farmacologia di sistemi quantitativi (QSP), basata sulla modellazione computazionale nelle neuroscienze, combinata con una corretta individuazione e farmacologia del target dei farmaci, patologia umana, studi di imaging, e calibrazione e validazione mediante studi clinici in soggetti umani possa migliorare il tasso di successo dei progetti di ricerca e sviluppo sul sistema nervoso centrale (CNS R&D). Suggeriamo che una migliore comprensione delle interazioni di circuiti neuronali utilizzando un’integrazione della conoscenza sulla fisiologia e farmacologia basata su computer umanizzati possa sostanzialmente annullare i rischi per nuovi progetti sul sistema nervoso centrale.

Nel testo:

The successful development of novel first-in-class therapeutic agents in the CNS has been lagging with respect to other disease areas. Moreover, only 8% of CNS drugs that enter phase 1 are approved,1 with about 65% of the failures due to lack of efficacy or sufficient differentiation in phase III.2 This high degree of failure is caused by the extreme complexity of the human brain neurobiology and the increasing realization that the clinical outcome is driven by emergent properties of neuronal circuits, rather than by a single target.

Complex translational problems that preclude simple animal model extrapolation3 in CNS R&D include (i) fundamental differences in neurotransmitter circuitry between rodents and humans, (ii) the incomplete representation of the full human pathology, (iii) the absence of important functional genotypes in animal models, and (iv) the existence of unique pharmacologically active human metabolites. Many of these problems can in principle be reduced using humanized computer-based QSP, as suggested by a white paper (http://isp.hms.harvard.edu/wordpress/wp-content/uploads/2011/10/
NIH-Systems-Pharma-Whitepaper-Sorger-et-al-2011.pdf
). Application of QSP in CNS disorders is particularly appealing because of the large academic expertise in computational neuroscience since the seminal paper of Hodgkin and Huxley,4 and due to the availability of the specialized software languages and the sharing of software modules (http://senselab.med.yale.edu/ModelDB/).

We will argue with a few examples that computer-based QSP (Figure 1) could be a powerful additional tool to reduce clinical attrition in psychiatry and Alzheimer’s disease, although in principle, this approach can be applied to other CNS diseases, such as Parkinson’s disease, Huntington’s disease, and cognitive and negative symptoms in schizophrenia.

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