Inborn error of metabolism (IEM) is a group of inherited disorders in which the body can’t metabolize specific nutrients or generate energy. This year we came up with a platform to diagnose multiple IEM diseases and we took phenylketonuria as a prototype. We aim to provide cost-effective, highly sensitive, and specific tests that are easy to implement in the real world. Accordingly, The team this year tried to identify all the upcoming issues that might arise and provided a well-studied plan for how our platform can be implemented.

Figure 1: IEM nosological classification:
1. Intoxication disorders: due to accumulation of toxic compounds as a result of metabolic blockage. The symptoms vary according to the type of accumulated compound, the stage and severity of the disease from symptoms free up to neurological disorders and death. PKU is the most common intoxication disorder.
2. Energy metabolism disorders: caused by defects in the production or the utilization of energy. It always affects organs with high energy consumption like the brain and skeletal muscle.
3. Storage disorders: results of alteration in synthesis or catabolism of large molecules that accumulate in the cell organelles leading to cell dysfunction and death.

And from a user's perspective, it starts from the moment of birth when you need to screen for any inborn metabolic error. Here, our detection kit can be used for fast screening, and after diagnosis, the most probable intervention can be performed as soon as possible to prevent any complications. The patient can then use the platform to monitor his condition since Egypt and Africa lack inexpensive detection and monitoring equipment. Additionally, we developed a gene delivery system that will allow the hepatic cells to produce the enzymes without interfering with the child's DNA.

Jury Line:

Machine learning guided aptamer Selection using Directed Evolution:
To select the best aptamer for our target we applied directed evolution methods by developing a machine learning tool that can identify the binding sites and the binding affinity between the Biomarker recognition element (aptamer) and the target analyte (phe) to predict the optimum structure and sequence of our aptamer.

Fugitive Line:

Whole-Cell-based Biosensor for optimum detection of excess analytes:
After the aptamer consumed the normal level of phenylalanine, the sample flows to the fugitive line containing a whole-cell biosensor that senses any excess amount of phenylalanine by producing B-galactosidase outside the cell to react with a pigment called X-gal which gives us blue color.

General Safety concerns & their solutions:
In terms of general safety measures, our whole cell system biosensor bacterial strain is non-toxic to humans, and the plastic case further protects the user from direct contact with the bacteria.

To make our test more sensitive we designed 3 line chip the first line is the Jury line that contains Au_NP labeled ssDNA bonded aptamers which consume the normal level of phenylalanine to reduce false positive results and make it more accurate, and the second line is the control line this line follows the fugitive line to insure that fugitive line is working and reduce false-negative.

To validate our test we will follow these steps:

1. We will start by applying our test on a standard phenylalanine sample and serum sample to compare these results with each other.
2. Then we will compare those results with tandem mass spectrometry results which is the standard test for PKU.

To be sure that we gave the patients the best care we developed a new therapeutic approach without more diet restrictions by implementing our novel E.coli system that gives the liver the ability to produce phenylalanine hydroxylase (PAH) without affecting the human DNA.

General Safety concerns & their solutions:
To validate our test we will follow these steps:

1. One of them was how to stop (PAH) synthesis when the phenylalanine is normal. So we added a CRISPER system to our circuit to cut the circuit when we reach the normal level of phenylalanine.
2. The second problem was how to deliver the (PAH) RNA from the E.coli system to liver cells to give them the ability to produce (PAH). We applied a novel gene delivery method called (Selective Endogenous eNcapsidation for cellular Delivery) or SEND platform to ensure that the (PAH) RNA found the target cell.

Home test kit as P-O-C (Point of care):
In short, we have developed a cheap, easy-to-use, and highly sensitive platform for detecting inborn metabolic errors early and preventing their complications from occurring.

Current challenges and future improvements:
Our team this year tried to identify all the upcoming issues that might arise and provided a well-studied plan for how our platform can be implemented. From the need for a quick, fast, sensitive to detect the IEM early to avoid their complications our idea came to light, and to help health care providers detect the disease as soon as possible, in addition to the parents or patients who want to monitor their condition since most of them do not have access to expensive laboratories. The WHO guidelines specify how testing instruments must be user-friendly and inexpensive for the target customers, which we referred to and complied with while developing the kit.

Shortly, we will develop a 3D-printed case for the diagnostic platform. A 3D printer can print out the model, making it even more effortless, while eco-friendly plastic is used, and we designed an overall user-friendly and adaptable system for testing. We also plan to produce the therapeutic system in the form of acid-resistant capsules shaped like gummy bears to make them more appealing to children. The system will be integrated with the diagnostic platform so that the child will be able to monitor his Phe levels after each meal.

All newborns must be investigated for IEM to take precautionary measures to avoid any possible complications. However, low-income countries can’t afford the cost of screening tests for all IEM. We aim to make these screening tests affordable to these countries by developing a new diagnostic chip and making it accessible to the whole world at low prices, especially in these countries. We started with PKU as a prototype for our diagnostic chip and we will start our project in Egypt by integrating it into a national newborn screening program then we will allocate it to other African countries and the whole world.

We plan to take our kit outside of a hospital lab in order to enable quick point-of-care testing. The kit can be used in many different places, including households, schools, and medical facilities.
The main beneficiaries of our kit are newborn infants because they need a quick diagnosis and a quick treatment plan to develop and grow as normally as their healthy peers. Because the diagnostic platform is affordable and user-friendly, it will be widely used in low-income countries in both homes and health facilities, increasing the number of actual beneficiaries. Following diagnosis, patients will receive their treatment plan via our therapeutic platform and monitor their Phe levels.

Additionally, doctors will be able to diagnose patients quickly and accurately without having to wait for lab results, which will result in a quick treatment plan.

In the end, parents won't need to follow any challenging processes to readily check the Phe levels of their kids at home.

As a result of the need for more equality among our societies, it became necessary to develop a color blind-friendly system. So we developed a software that analyzes the results by only uploading an image for the kit. As soon as the image is uploaded, the software begins to work. This tool examines the quality of the provided image, whether it is in an acceptable and clear format, or if it has poor quality, in which case it returns an error message and asks for another image with higher resolution and quality. Following the quality check of the image, the tool begins analyzing the test results. As a result, both the control line and the test line are calculated in terms of pixels. Using the tool, you will receive three outputs. An output first indicates whether the test is a Positive PKU test or a Negative PKU test by stating the sample band ratio. Secondly, a line graph shows the correlation between sample band ratios and time of analysis of the tool, as well as different fluctuations in the results. Finally, the test line is intensified for further confirmation by a second image.
PKU lateral flow assay software provides semi-quantification and can be adapted for other LFA tests by applying appropriate modifications depending on the disease or condition that the user is tackling.