Rational Design and In Vivo Characterization of mRNA-Encoded Broadly Neutralizing Antibody Combinations against HIV-1.
Elisabeth NarayananSamantha FalconeSayda M ElbashirHusain AttarwalaKimberly HassettMichael S SeamanAndrea CarfiSunny HimansuPublished in: Antibodies (Basel, Switzerland) (2022)
Monoclonal antibodies have been used successfully as recombinant protein therapy; however, for HIV, multiple broadly neutralizing antibodies may be necessary. We used the mRNA-LNP platform for in vivo co-expression of 3 broadly neutralizing antibodies, PGDM1400, PGT121, and N6, directed against the HIV-1 envelope protein. mRNA-encoded HIV-1 antibodies were engineered as single-chain Fc (scFv-Fc) to overcome heavy- and light-chain mismatch. In vitro neutralization breadth and potency of the constructs were compared to their parental IgG form. We assessed the ability of these scFv-Fcs to be expressed individually and in combination in vivo, and neutralization and pharmacokinetics were compared to the corresponding full-length IgGs. Single-chain PGDM1400 and PGT121 exhibited neutralization potency comparable to parental IgG, achieving peak systemic concentrations ≥ 30.81 μg/mL in mice; full-length N6 IgG achieved a peak concentration of 974 μg/mL, but did not tolerate single-chain conversion. The mRNA combination encoding full-length N6 IgG and single-chain PGDM1400 and PGT121 was efficiently expressed in mice, achieving high systemic concentration and desired neutralization potency. Analysis of mice sera demonstrated each antibody contributed towards neutralization of multiple HIV-1 pseudoviruses. Together, these data show that the mRNA-LNP platform provides a promising approach for antibody-based HIV treatment and is well-suited for development of combination therapeutics.
Keyphrases
- antiretroviral therapy
- hiv positive
- hiv infected
- hiv testing
- human immunodeficiency virus
- hepatitis c virus
- hiv aids
- men who have sex with men
- binding protein
- type diabetes
- poor prognosis
- high fat diet induced
- skeletal muscle
- stem cells
- zika virus
- big data
- mesenchymal stem cells
- machine learning
- bone marrow
- replacement therapy
- aedes aegypti