Abstract

Background: Recombinant antibody fragments are promising alternatives to full-length immunoglobulins and offer important advantages compared with conventional monoclonal antibodies: extreme specificity, higher affinity, superior stability and solubility, reduced immunogenicity as well as easy and inexpensive large-scale production.

Objective: In this article we will review and discuss recombinant antibodies that are being evaluated for neurodegenerative diseases in pre-clinical models and in clinical studies and will summarize new strategies that are being developed to optimize their stability, specificity and potency for advancing their use.

Methods: Articles describing recombinant antibody fragments used for neurological diseases were selected (PubMed) and evaluated for their significance.

Results: Different antibody formats such as single-chain fragment variable (scFv), single-domain antibody fragments (VHHs or sdAbs), bispecific antibodies (bsAbs), intrabodies and nanobodies, are currently being studied in pre-clinical models of cancer as well as infectious and autoimmune diseases and many of them are being tested as therapeutics in clinical trials. Immunotherapy approaches have shown therapeutic efficacy in several animal models of Alzheimer´s disease (AD), Parkinson disease (PD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), Huntington disease (HD), transmissible spongiform encephalopathies (TSEs) and multiple sclerosis (MS). It has been demonstrated that recombinant antibody fragments may neutralize toxic extra- and intracellular misfolded proteins involved in the pathogenesis of AD, PD, DLB, FTD, HD or TSEs and may target toxic immune cells participating in the pathogenesis of MS.

Conclusion: Recombinant antibody fragments represent a promising tool for the development of antibody-based immunotherapeutics for neurodegenerative diseases.

Keywords: Recombinant antibody fragments, nanobody, intrabody, prion protein, alzheimer´s disease, parkinson disease, Huntington disease

Graphical Abstract