Kevin Mayer Senor Editor Genetic Engineering & Biotechnology News

Mix-and-match peripherals on a common platform may support myriad tests.

If you had to pick the most compelling mHealth, or mobile health, application, you might well pick mDiagnostics—the ability to perform laboratory work outside the laboratory. mDiagnostics promises to bring on-the-spot testing to poor and remote areas where conventional tools such as microscopes, cytometers, and colorimeters are unavailable.

Mobile, off-the-grid diagnostic tools are best compact. But, beyond that, choices abound. mDiagnostic tools could be sleek, self-contained, standalone devices. Or they could be modular. For example, an mDiagnostic tool could consist of an add-on device plugged into a general-purpose platform, the smartphone.

Over the past couple of years, smartphone-based devices have been introduced by various developers. Invariably, the developers declare that the smartphone is the ultimate mDiagnosis platform. After all, they say, the smartphone is already ubiquitous. Moreover, it has computational capacity to spare, as well as a built-in camera. Finally, as a GPS-enabled communications device, a smartphone platform could readily integrate point-of-care diagnostics with public-health projects, such as mapping the spread of an infectious disease.

All of this is plausible. But it still misses what may be the strongest argument for smartphone-based diagnostics—the smartphone-centric approach efficiently deploys developer resources. This approach may lead to a thriving development ecosystem.

Curiously, the idea of cultivating a development ecosystem is seldom presented by developers. Recently, however, this idea was clearly articulated by academic researchers, in “Biosensing with cell phones,” an article that appeared in the journal Trends in Biotechnology.

Any reader willing to endure the academese will readily discern the argument’s outline: 1) If you need “apps,” you need application developers. 2) To attract the services of as many application developers as possible, use a familiar, open platform. (With such a platform, developers needn’t be hardware specialists—modularized hardware elements conform to the same standards, so they readily communicate with each other.) 3) Developers, empowered to work with any hardware configuration, may focus on their “value add.”

This last point, of course, is where things get interesting. It is, most commonly, where developers address the needs of specific market segments. In mDiagnostics, it is where developers may focus on particular kinds of tests, which typically rely on specialized biosensors.

Needless to say, no standalone mDiagnostic device could ever stand in for an entire laboratory. Instead, multiple devices would have to stand in for multiple pieces of equipment, each dedicated to a different kind of test. Here, again, the modular approach makes sense. It lets mDiagnostic practitioners hold onto a core device—the smartphone—while plugging in add-on devices as needed. The add-on devices may even be disposable.

Where Seemingly Ramshackle Devices Shine

If you are a highly trained medical professional that enjoys ready access to the services of a full-service laboratory, you may be underwhelmed by the thought of using a handheld biosensor, which would probably be optimized to run a particular diagnostic test or, at best, a tidy collection of tests. Could any such handheld biosensor be worth the bother, however quickly it returned results? Better to simply add a few checks to an order form, send your request to a laboratory, and accept that there might be a short delay.

But you may be a medical professional working in a resource-constrained environment, where you might not even be confident that you’ll see a patient more than once. Or you may be charged with planning medical responses to natural or human-made disasters, which might disrupt usual testing procedures. Or you might have to cope with the slow-motion disaster of increasingly tight budgets, in which case you might try implementing a distributed testing model.

In each of these situations, an inexpensive pocket-sized biosensor might be an attractive option. It might even coincide with patients’ desires to participate in their own care. In particular, elderly patients or caretakers for the elderly might appreciate being able to monitor multiple chronic conditions with over-the-counter biosensors.

The precedent here, of course, is the portable blood glucose monitor. It demonstrates that patients are capable of embracing point-of-care testing that is at least somewhat invasive. They are not necessarily waiting for a Star Trek-style Tricorder, although it does happen that an X-prize program has been launched for such a device. It remains to be seen whether comprehensive testing may be accomplished without drawing so much as a drop of blood. Tests relying on breath, tears, sweat, or saliva have been investigated, but they have had only limited success.

Besides having potential clinical applications, portable biosensors may advance research, for example, by assisting study participants. Crowdsourced science, too, is a possibility. Yet other applications may be found in environmental testing (air/water quality testing, food inspection, remediation) and security/law enforcement.

Public Health Applications

Even in a resource-rich environment, portable biosensors may prove useful in tracking disease outbreaks and identifying mysterious pathogens—not that there aren’t any compelling alternatives to highly distributed and even improvisational biosensor networks. One fairly top-down alternative, the Threat Net, could cover the United States with just 200 devices, provided they were strategically deployed in existing medical centers. Threat Net devices, laboratory machines that can identify any of more than 1,000 pathogens, rely on mass spectrometry and sophisticated algorithms to sift through genetic material from patient samples.

Threat Net’s leading advocate is David J. Ecker, Ph.D., a scientist at Ibis Biosciences. In a recent issue of Scientific American, he was quoted as follows: “Everywhere I’ve briefed this notion, including the White House national security staff, everyone has been enthusiastic. The problem is this is nobody’s job to think big like this and then find a way to piece it together.”

If, as Dr. Ecker has suggested, a project such as Threat Net would be especially hard to manage in countries with decentralized and mostly private healthcare systems, a distributed and even ramshackle mHealth approach might be an option. It wouldn’t be pretty, since it would rely on a scattering of diverse platforms, none of them as elegant as a mass spectrometer. (Such a device would hardly fit in your pocket.) And it would have lots of gaps and discontinuities. But it wouldn’t require anyone to think big or create a comprehensive plan. This, in some philosophies, is a virtue.

Examples of Smartphone-Based Diagnostic Devices

Several portable biosensor devices have been developed over the past couple of years. All of the devices listed below are based on smartphone platforms.

University of California, Berkeley
A biosensor that detects toxic chemical vapors or airborne pathogens.

To develop a mobile app called the iColour Analyser, UC Berkeley researchers employed a technique they pioneered to mimic nanostructures like collagen fibers. The researchers found a way to get M13 bacteriophages, benign viruses with a shape that closely resembles collagen fibers, to self-assemble into patterns that could be easily fine-tuned.

When the collagen-like phage bundles are exposed to target chemicals, they expand or contract, generating different colors. While initial tests focused on detecting explosives and their precursors, the developers hope to use their technology to create a breath test to detect cancer and other diseases.

University of California, Los Angeles
A rapid diagnostic test (RDT) reader that relieves operators of the need to judge color strips by eye.

Where conventional tools such as microscopes and cytometers are unavailable, it is still possible to resort to RDTs, small strips on which blood or fluid samples are placed. Changes in the color of the strip indicate the presence of infection.

The RDT reader developed by the UCLA researchers clips onto a cell phone; weighs approximately 65 grams; and includes an inexpensive lens, three LED arrays, and two AAA batteries. The platform, which has the ability to read nearly every type of RDT, produces a digital (and nonfading) image of the RDT. Also, test results may be sent to a global server that can create maps and chart the spread of a disease.

University of Illinois at Urbana-Champaign
A wedge-shaped cradle and app for the iPhone that uses the phone’s built-in camera and processing power a biosensor to detect toxins, proteins, bacteria, viruses, and various target molecules.

At the heart of this biosensor is a photonic crystal, which works something like a mirror, reflecting on wavelength of light while the rest of the spectrum passes through. When anything biological attaches to a slide coated with photonic material, the reflected color will shift from a shorter wavelength to a longer one. The degree of the shift indicates how much of the target molecule is in the sample.

The cradle holds about $200 worth of optical components. An Android-compatible version is in the works, as is a multimode biosensor.

University of Rhode Island
Blood-testing technology that incorporates a smartphone application, a handheld biosensor, and a credit card-sized cartridge.

To operate this lab-on-a-chip system, users place a drop of blood from a finger prick on a disposable plastic polymer cartridge and insert it into the handheld biosensor. The blood travels through the cartridge in tiny channels 500 microns wide to a detection site where it reacts with preloaded reagents, enabling the fluorescence sensor to detect certain biomarkers of disease.

The first cartridges focus on the detection of C-reactive proteins in the blood, markers of cardiovascular and peripheral vascular diseases. Additional cartridges can be engineered to detect biomarkers of other diseases, including the beta amyloid protein that can be used as a predictor of Alzheimer’s disease. The device can also be developed to detect virulent pathogens, such as HIV, hepatitis B, and H1N1 (swine) flu. The next generation of the device, currently in development, will put the entire lab on paper, eliminating the need for active pumping of blood and reagents through the cartridge.

Cornell University
A smartphone accessory that optically detects biomarkers in a drop of blood, sweat, or saliva.

The smartCARD accessory, which looks somewhat like a smartphone credit card reader, clamps over the phone’s camera. In one application, smartCARD lets users put a drop of blood on a cholesterol test strip. Then the device processes the blood through separation steps and chemical reactions, preparing the strip for colorimetric analysis. The device’s built-in flash provides uniform, diffused light to illuminate the test strip, and an application in the phone calibrates the hue saturation and generates results for display.

A handheld device that can detect disease antigens in samples from serum, urine, or saliva and connects with smartphones via Bluetooth.

OJ-Bio has developed immunoassay-based biosensors for the detection of three respiratory virus pathogens such as respiratory syncytial virus, influenza A, and influenza B. It enables one chip to detect three different viruses at the same time. In addition to respiratory diseases, the company is currently involved in clinical development programs for point-of-care diagnosis of HIV and periodontal gum disease.

The biosensor relies on surface acoustic wave (SAW) technology. Specifically, SAW chips are coated with disease-specific biocapture surfaces. Analyte binding causes a shift in the phase angle of the surface acoustic wave passing across the chip surface, and this is translated into an electronic signal.

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