Biology subjects

Ketchem, R. R.

Publications and source records attributed to Ketchem, R. R..

5 recordsLinked to original sources

bioRxiv2025

Structural characterization of MAM01 and other cost-effective engineered monoclonal antibodies for malaria prevention

Long-lasting and effective vaccines or monoclonal antibodies (mAbs) for malaria prevention are highly beneficial for people of all ages living in malaria-endemic regions. Recently, two highly protective human mAbs AB000224 (IGHV3-49/IGLV1-40) and AB007088 (IGHV3-33/IGKV1-5), which are encoded by different germline genes, were re-engineered and renamed as MAM01 and MS-1805, respectively, to improve their half-life, developability (including manufacturing), and cost-effectiveness according to WHO guidelines. MAM01 has completed phase 1 and 1b clinical trials (safety / PK / challenge) in US healthy naive adults and is in phase 1 trials (age-de-escalation studies) in Uganda. Here, we determined crystal structures of the antigen-binding fragments (Fabs) of engineered MAM01, MS-1805, and 7088 in complex with different regions of the Plasmodium falciparum (Pf) circumsporozoite protein (CSP), including junctional, minor, and major repeat regions. Notably, Fab 7088 features an extended CDRL3 comprising 10 amino acids (CDRL3:10) instead of the typical 8 amino acid CDRL3 (CDRL3:8) for VH3-33/VK1-5-encoded mAbs, revealing unique folding within its germline context. Structural comparisons showed that engineered antibodies retain the key molecular and hydrogen bond interactions with no significant conformational changes or loss in binding affinity. These findings demonstrate that the efficacy and affordability of human mAbs can be enhanced by selectively mutating residues in antibody framework regions without compromising binding affinity or epitope interaction.

biophysics↗

bioRxiv2024

A Comprehensive Engineering Strategy Improves Potency and Manufacturability of a Near Pan-neutralizing Antibody Against HIV

Anti-HIV envelope broadly neutralizing antibodies (bnAbs) are alternatives to conventional antiretrovirals with the potential to prevent and treat infection, reduce latent reservoirs, and/or mediate a functional cure. Clinical trials with "first generation" bnAbs used alone or in combination show promising antiviral effects but also highlight that additional engineering of "enhanced" antibodies will be required for optimal clinical utility, while preserving or enhancing cGMP manufacturing capability. Here we report the engineering of an anti-CD4 binding-site (CD4bs) bnAb, N49P9.3, purified from the plasma of an HIV elite-neutralizer. Through a series of rational modifications we produced a variant that demonstrates: enhanced potency; superior antiviral activity in combination with other bnAbs; low polyreactivity; and longer circulating half-life. Additional engineering for manufacturing produced a final variant, eN49P9, with properties conducive to cGMP production. Overall, these efforts demonstrate the feasibility of developing enhanced anti-CD4bs bnAbs with greatly improved antiviral properties as well as potential translational value.

bioengineering↗

bioRxiv2024

Somatic hypermutation-mediated paratope flexibility improves the cross-reactivity of human malaria antibodies

The protective capacity of antibodies targeting circumsporozoite protein on sporozoites of the malaria parasite Plasmodium falciparum (PfCSP) is linked to high affinity and cross-reactivity with the PfCSP central repeat domain and N-terminal junction. However, the role of somatic hypermutation (SHM) in the development of such antibodies remains unclear. Here we define the contributions of SHM to the high affinity and strong repeat and N-junction cross-reactivity of the potent anti-PfCSP monoclonal antibody (mAb) 4493 and of similar antibodies with shared SHM and affinity maturation trajectories. Molecular dynamics simulations reveal that SHM reduces the flexibility of the unbound mAb 4493 but increases the flexibility of the antigen-bound complex, thereby lowering the entropic cost for antigen binding. Furthermore, we identify an inverse relation between antibody affinity and serum stability, which limits the protective capacity of these antibodies. Our study provides molecular level evidence for the different roles that the SHM process plays in increasing VH3-49+V{kappa}3-20 antibody affinity and cross-reactivity and demonstrates how antibody affinity maturation can negatively impact antibody stability and thereby function.

immunology↗

bioRxiv2023

AI-based antibody discovery platform identifies novel, diverse and pharmacologically active therapeutic antibodies against multiple SARS-CoV-2 strains

A critical aspect of a successful pandemic response is expedient antibody discovery, manufacturing and deployment of effective lifesaving treatments to patients around the world. However, typical drug discovery and development is a lengthy multi-step process that must align drug efficacy with multiple developability criteria and can take years to complete. In this context, artificial intelligence (AI), and especially machine learning (ML), have great potential to accelerate and improve the optimization of therapeutics, increasing their activity and safety as well as decreasing their development time and manufacturing costs. Here we present a novel, cost-effective and accelerated approach to therapeutic antibody discovery, that couples AI-designed human antibody libraries, biased for improved developability attributes with high throughput and sensitive screening technologies. The applicability of our platform for effective therapeutic antibody discovery is demonstrated here with the identification of a panel of human monoclonal antibodies that are novel, diverse and pharmacologically active. These first-generation antibodies, without the need for affinity maturation, bind to the SARS-CoV-2 spike protein with therapeutically-relevant specificity and affinity and display neutralization of SARS-CoV-2 viral infectivity across multiple strains. Altogether, this platform is well suited for rapid response to infectious threats, such as pandemic response. IMPORTANCEExpedient discovery and manufacturing of lifesaving therapeutics is critical for pandemic response. The recent COVID pandemic has highlighted the current inefficiencies and the need for improvements. To this end, we present our therapeutic antibody discovery platform that couples artificial intelligence (AI) and innovative high throughput technologies, and we demonstrate its applicability to rapid response. This platform enabled the isolation, characterization, and rapid identification of effective broadly neutralizing SARS-CoV-2 antibodies with good developability attributes, anticipated to fit our current process development and manufacturing platform. As such, this would benefit cost-of-goods and improve therapeutic access to patients. The AI-derived antibodies represent an advantageous therapeutic modality that can be developed and deployed fast, thus well suited for rapid response to infectious threats, such as pandemic response.

immunology↗

bioRxiv2020

Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks

We demonstrate the use of a Generative Adversarial Network (GAN), trained from a set of over 400,000 light and heavy chain human antibody sequences, to learn the rules of human antibody formation. The resulting model surpasses common in silico techniques by capturing residue diversity throughout the variable region, and is capable of generating extremely large, diverse libraries of novel antibodies that mimic somatically hypermutated human repertoire response. This method permits us to rationally design de novo humanoid antibody libraries with explicit control over various properties of our discovery library. Through transfer learning, we are able to bias the GAN to generate molecules with key properties of interest such as improved stability and developability, lower predicted MHC Class II binding, and specific complementarity-determining region (CDR) characteristics. These approaches also provide a mechanism to better study the complex relationships between antibody sequence and molecular behavior, both in vitro and in vivo. We validate our method by successfully expressing a proof-of-concept library of nearly 100,000 GAN-generated antibodies via phage display. We present the sequences and homology-model structures of example generated antibodies expressed in stable CHO pools and evaluated across multiple biophysical properties. The creation of discovery libraries using our in silico approach allows for the control of pharmaceutical properties such that these therapeutic antibodies can provide a more rapid and cost-effective response to biological threats.

immunology↗