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Updated: May 24, 2026

Laboratory Scale Production and Purification of a Therapeutic Antibody
Published on: January 24, 2017
Single cycle structure-based humanization of an anti-nerve growth factor therapeutic antibody
Sonia Covaceuszach1, Sara Marinelli, Ivet Krastanova
1Lay Line Genomics, SpA, Roma, Italy.
Abstract:
Most forms of chronic pain are inadequately treated by present therapeutic options. Compelling evidence has accumulated, demonstrating that Nerve Growth Factor (NGF) is a key modulator of inflammatory and nociceptive responses, and is a promising target for the treatment of human pathologies linked to chronic and inflammatory pain. There is therefore a growing interest in the development of therapeutic molecules antagonising the NGF pathway and its nociceptor sensitization actions, among which function-blocking anti-NGF antibodies are particularly relevant candidates.In this respect, the rat anti-NGF αD11 monoclonal antibody (mAb) is a potent antagonist, able to effectively antagonize rodent and human NGF in a variety of in vitro and in vivo systems. Here we show that mAb αD11 displays a significant analgesic effect in two different models of persistent pain in mice, with a remarkable long-lasting activity. In order to advance αD11 mAb towards its clinical application in man, anti-NGF αD11 mAb was humanized by applying a novel single cycle strategy based on the a priori experimental determination of the crystal and molecular structure of the parental Fragment antigen-binding (Fab). The humanized antibody (hum-αD11) was tested in vitro and in vivo, showing that the binding mode and the NGF neutralizing biological activities of the parental antibody are fully preserved, with even a significant affinity improvement. The results firmly establish hum-αD11 as a lead candidate for clinical applications in a therapeutic area with a severe unmet medical need. More generally, the single-cycle structure-based humanization method represents a considerable improvement over the standard humanization methods, which are intrinsically empirical and require several refinement cycles.
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