Introduction and Background of the Project

Introduction

Malaria kills more than 600,000 people per year, most of them children. Malaria disproportionately impacts pregnant women, resulting in anemia, fetal loss, and low birth weight, which is itself associated with increased risk of death and poor developmental outcomes. The gap the project team seeks to address lies in addressing critical needs in malaria diagnosis. Malaria treatment requires a diagnosis that distinguishes malaria from other diseases with similar symptoms, and the correct identification of the malaria parasite species: P. falciparum, non-falciparum parasites, and increasingly, P. knowlesi, which has emerged as an important cause of malaria infections in Southeast Asia. At present there is no satisfactory diagnostic test that distinguishes infection with P. knowlesi from infection with P. vivax and other, related malaria parasite species, which is critical for early diagnosis and treatment. The design and implementation of highly sensitive rapid diagnostics such as easy to use lateral flow assays would be hugely beneficial for the diagnosis and treatment of malaria. 

Project objective

Current malaria Lateral Flow Assays use antibodies as their capture and detection reagents. The limited sensitivity of LFAs in part reflects the relativity modest affinities of typical antibodies for their target proteins. In addition, the identification of antibodies that target related proteins with high specificity is time-consuming and uncertain of success. A technology that replaces monoclonal antibodies could thus have a transformative impact on the development of new diagnostics. the project team proposes to develop novel protein-based affinity reagents that enable the creation of rapid diagnostic tests (RDTs) that diagnose infection with Plasmodium falciparum, non-falciparum malaria parasites, and the emerging pathogen Plasmodium knowlesi with sensitivities that considerably exceed existing alternatives. This work will result in affinity reagents that integrate directly into the existing infrastructure for the creation of lateral flow assays (LFAs), the technology used in rapid COVID tests. The products of this project can thus be rapidly scaled to impact malaria treatment worldwide. As part of this project, the project team will validate the approach using clinical samples. If successful, the diagnostics resulting from this collaboration will possess enhanced sensitivity, greater shelf stability, lower cost, and the elimination of test failure due to parasite mutations, four improvements that have been identified as critically important by the World Health Organization (WHO) for the management of malaria. Success in this project will establish a general-purpose platform for the rapid development of highly sensitive diagnostic assays. In addition to its impact on malaria diagnosis and treatment, this project will thus provide proof-of-concept for a novel platform technology enabling the development of rapidly scalable diagnostics for novel infectious diseases. The overall goals are thus to develop new protein-based affinity reagents that can improve the sensitivity of malaria LFAs, and to extend malaria LFAs to include diagnosis of infection with P. knowlesi.

Project design

The overall strategy is to replace the antibodies that are used in conventional LFAs with computationally designed binders based on the monobody scaffold. The nanomolar (nM) affinities of most antibodies limits the sensitivity of LFAs. To circumvent this limitation, the project team will use de novo protein design to identify numerous monobody binders for a target malaria antigen of interest. We will then link monobodies together to generate bivalent binders for the malaria antigens. This strategy makes use of the well-known principle of avidity, in which linear improvements in binding energy yield exponential improvements in dissociation constants: both empirical evidence and simple calculations indicate that combining monobody binders with 10-100 nM KD values should yield multivalent binders with KD values in the pM regime. Importantly, this strategy is made possible by the unique ability of computational design to deliberately target multiple, distinct sites on the target protein. The project team will quantify the binding affinities and cross-reactivity of bivalent monobodies and confirm that they possess the properties required for use in an LFA. Finally, the project team will assess the performance of the resulting capture and detection reagents in prototype LFAs.

How can your partnership (project) address global health challenges?

This project addresses three critical needs identified by the WHO and the malaria community: 1) a sensitive rapid diagnostic test (RDT) that can detect P. falciparum harboring Hrp2/3 deletions, 2) development of RDTs with sensitivities comparable to microscopy, and 3) the creation of a sensitive and species-specific RDT for the emerging pathogen P. knowlesi. 

As stated in the WHO World Malaria Report 2022 “…the spread of P. falciparum parasites with Pfhrp2/3 gene deletions presents a major threat to reliable diagnosis.”. In principle, this challenge can be met using LFAs that detect Plasmodium LDH. However, present tests targeting Plasmodium LDHs are severely lacking in sensitivity, and the sole available pre-qualified test for P. falciparum LDH does not meet the WHO criterion of sensitivities corresponding to the detection of 200 parasites/μL of blood. The project team will leverage the unique strengths of de novo protein design to generate LFAs that detect P. falciparum LDH with sensitivities similar to that of microscopy (10 parasites / μL). The project team will use the same pipeline to generate a LFA for non-falciparum LDH, which can be used to diagnose infection with P. vivax and other malaria species. Success in these objectives could thus lead to a transformative advance in the diagnosis and treatment of malaria in sub-Saharan Africa and Southeast Asia.

P. knowlesi is an emerging and worsening zoonotic malaria parasite in Southeast Asia, and there is increasing concern that human-to-human transmission could lead to expansion of the disease. The development of cheap point-of-care diagnostics is essential to diagnose knowlesi patients as early as possible. The diagnostics landscape for this species is currently sub-optimal, and there remains no P. knowlesi-specific RDT available.  A rapid, easy, and effective point-of-care diagnostic for P. knowlesi, as proposed here, would thus be a game-changer in the control and management of this emerging disease.

What sort of innovation are you bringing in your project?

Computational protein design offers a powerful new means of generating affinity reagents. The project team has developed powerful new machine-learning based methods that enable the computational design of protein binders to a target of choice. The designed binders are based on a monobody scaffold, which is smaller, more thermostable, and less costly to produce compared to antibodies. Their enhanced stability relative to antibodies is anticipated to be valuable in the context of malaria diagnostics, which must be robust to field conditions. De novo computational protein design offers the unique capability to generate binders for a given target of choice with exceptionally high binding affinities. Here, the project team will leverage this new capability to generate protein-based binders for malaria antigens that can be used in place of antibodies in malaria LFAs. The project team also have deep expertise in P. knowlesi biology and epidemiology, cell-free protein expression, and malaria parasite proteomics.

Role and Responsibility of Each Partner

Ehime University (EU) will lead the development of computationally designed proteins that bind tightly to malaria-specific antigens, namely lactate dehydrogenase (LDH) from P. falciparum and non-falciparum malaria strains (e.g. P. vivax), and antigens specific to P. knowlesi that will be identified as part of this project through sub-contracting to Stanford University. EU will also lead the laboratory-scale validation of optimized binders via ELISA and half-strip LFA assays, including testing binding of the computationally designed binders, termed monobodies, to recombinant proteins by ELISA. P. falciparum LDH, non-falciparum LDH, and P. knowlesi antigens will be expressed using the wheat-germ cell-free protein expression system, a system invented and refined at Ehime University that is particularly well-suited to the production of malaria parasite proteins.

Nagasaki University (NU) will perform mass-spectrometry analysis of the samples obtained from in vitro cultured lines of P. knowlesi and P. cynomolgi (representative of non-falciparum/non-knowlesi malaria parasites) and knowlesi malaria patients to identify antigens from P. knowlesi suitable for use in the development of LFAs. The Kaneko laboratory will assist in the validation of reagents targeting these antigens and will participate in proof-of-concept validation of the malaria diagnostic assays using half-strip LFAs and patient-derived samples for which the laboratory has access.

Universiti Malaysia Sabah (UMS) will conduct application of dipstick (half-strip) assays using patient-derived samples which will be collected for this project. Should the project team manage to progress to the production of a prototype RDT, UMS will test it at the Point of Care (POC) using patient samples.