– Rohini Murugan
Acquired Immune Deficiency Syndrome, or AIDS, is a chronic, life-threatening condition that has cost around 40 million lives worldwide since it was first reported in 1981. It is caused by the Human Immunodeficiency Virus (HIV) which is known for its ability to evade the host’s immune system effectively. Since there is no known cure or vaccine for this disease, the treatment requires a prolonged course of anti-retroviral therapy (ART).
ART involves a cocktail of drugs that reduces (but does not eliminate) the viral load in the body while preventing further transmission. A lifelong treatment with ART is usually necessary in the event of the infection being reignited again. However, a study done in macaques found that the probability of such an event occurring was significantly reduced through early immunisation with HIV antibodies (bNAb therapy).
In a recent study, researchers led by Narendra Dixit in the Department of Chemical Engineering, IISc, have constructed a novel mathematical model of HIV infection following immunisation with bNAb antibodies. Their model suggests that long-lasting reduction of viral load, one of the intrinsic states of the disease, is switched on by interventions like ART or bNAb therapy. Further, it also predicts that early bNAb therapy enhances the stimulation of the host’s immune cells and thus helps in mounting a better defense against the virus as compared to ART.
The researchers say that their model is the first quantitative description of the HIV dynamics under bNAb therapy and it unravels the mechanism underlying the response described in the in vivo macaque study. Since a prolonged ART treatment regime compromises the quality of life of the patient, the study adds to the evidence that bNAb therapy may be a promising alternative to ART as researchers seek to find a functional cure for the disease.
Desikan R, Raja R, Dixit NM. “Early exposure to broadly neutralizing antibodies may trigger a dynamical switch from progressive disease to lasting control of SHIV infection”. PLOS Computational Biology (2020) 16(8):e1008064. https://doi.org/10.1371/journal.pcbi.1008064