# The multiplexed single-tier InBios Lyme Detect Multiplex ELISA is more sensitive than standard two-tier tests in the early stages of Lyme disease

## TL;DR

A new Lyme disease test is more sensitive in early stages, potentially reducing false negatives and improving early treatment.

## Contribution

The InBios Lyme Detect Multiplex ELISA uses a multiplexed antibody array and machine learning to improve early detection of Lyme disease.

## Key findings

- The InBios assay detected 21 clinically diagnosed cases missed by standard tests.
- It showed high reproducibility and correctly identified all standard test-positive samples.
- The test had one false positive among 66 look-alike disease samples and 146 healthy controls.

## Abstract

There are nearly 500,000 cases of Lyme disease each year in the United States; 10%–20% of them result in the development of a debilitating chronic disease known as post-treatment Lyme disease. Existing standardized and modified two-tier tests (STT/MTT) suffer from poor detection rates in the first weeks of infection, where the antibody response, the basis of diagnosis, is developing but is not robust enough for detection. During this early window, false negative results are common, which leads to delayed treatment and increases the likelihood of developing severe symptoms. The InBios Lyme Detect Multiplex ELISA is a microarray-based assay designed to capture a set of commonly used diagnostic antibodies specific to Borrelia burgdorferi from human serum. The multiplex array captures common diagnostic antibodies, including those to C6, VlsE, and OspC, and has in-line controls. Diagnostic index scores are calculated from the relative abundance of controls and antibodies using a proprietary machine learning algorithm. The assay was evaluated here for reproducibility, accuracy, and performance. It was found to be reproducible using a group of 30 samples run in triplicate. The assay performed well in a blinded panel, correctly identifying all standard two-tier test-positive samples and controls while also detecting 21 of 79 samples that were clinically diagnosed but undetectable by standard Lyme serologic tests. There was one false positive from 66 look-alike disease samples and 146 healthy controls. The InBios assay has the potential to improve diagnostic sensitivity within the early weeks of infection while matching the specificity of current diagnostic tests.

During initial Lyme disease infection, existing diagnostic tests have poor sensitivity, resulting in a high number of false-negative tests. This is due to the lag between infection and a robust immune response capable of being detected by such tests. With a multiplexed array of nine unique antibody targets specific for Borrelia burgdorferi, interpreted by a proprietary machine learning algorithm, the InBios Lyme Detect Multiplex ELISA has the potential to increase diagnostic sensitivity within the first few weeks of infection, reducing the number of false-negative tests. Improving diagnostic sensitivity during early infection would reduce the risk of developing severe symptoms, including post-treatment Lyme disease.