Preclinical Assessment of a Cannabinoid CB2 Receptor Antagonist in a Murine Model of Cerebral Malaria

Abstract

Malaria is a most important parasitic disease due to its highest impact worldwide. It results in around 200 million clinical cases and 0,5-1 million deaths per year, mainly due to cerebral malaria (CM), a life-threatening neurological syndrome that predominantly affects predominantly children under five years old. CM follows neurological alterations leading to the death if left untreated, and, even when it is treated, it is fatal in 15-20% of cases. Moreover, among the survivors, more than 10% of the children develop neurological sequelae. Consequently, there is an urgent need to find therapies to attenuate these neurological signs. Recent evidence has proposed the endocannabinoid system, which plays an important neuromodulatory function in the central nervous system (CNS), also including immunomodulation preferentially exerted by CB2 receptor. Previous studies have shown that the genetic ablation of this receptor improved mice survival against CM, suggesting a potential for the pharmacological treatment of CM with selective antagonists of this receptor. Considering this background, we investigated CM therapy by a classic CB2 antagonist SR144528 in a murine model of the disease. First, we carried out binding studies with SR144528 to confirm its pharmacodynamic profile (binding affinity [Ki] value = 2.34 ± 0.61 nM; and efficacy [IC50] = 96.17 ± 1.41 nM, at the CB2 receptor). Second, P. berghei ANKA infected C57BL/6 mice were treated daily with SR144528 and assessed for parasitemia growth and neurological alterations. 30% of the treated mice showed partial recovery of CM symptoms with 20% increased survival, but finally succumbing to hyperparasitemia and severe anemia. These preliminary preclinical results suggest that, although part of the CM course might be modulated by the pharmacological blockade of the CB2 receptor, other elements trigger the lethal outcome. Thus, while our hypothesis could not be completely validated in this CM model, we detail here all obtained results for further research.