Appendix A_Program and Technical Description.pdf

APPENDIX A: PROGRAM AND TECHNICAL DESCRIPTION

1. BACKGROUND

The United States depends heavily on global pharmaceutical supply chains for key starting materials, active pharmaceutical ingredients, and finished drug products. As this dependence has grown, the challenge of ensuring the quality and safety of imported pharmaceuticals has also grown with it. Existing oversight tools—including facility inspections and laboratory-based testing—remain essential, but they are stretched to their limits supporting the magnitude and complexity of today’s international supply chains.

Current testing approaches for pharmaceutical quality and contamination are generally labor intensive, destructive, and slow. They work well when used in manufacturing facilities or independent testing labs for quality assurance. Conventional laboratory methods may require samples to be opened, dissolved, or otherwise processed under tightly controlled conditions, often taking weeks to return results. These methods are valuable for confirmation and enforcement too but are not well suited for high-throughput screening at points of entry or other operational environments. At the same time, many FDA warning letters cite data integrity or documentation problems, and the Agency has even made public statements regarding testing labs that have repeatedly provided falsified or misleading data as part of submissions to the agency.

Relying on inspections for quality assurance, a relatively small number of imported pharmaceuticals are selected on a risk-basis for direct quality and safety testing. Often, the same lab methods are used to characterize the products, which increases time and cost burden for all parties. There are several non-contact systems that can detect the presence of certain chemicals, but they have specificity and accuracy limitations, especially through packaging – which limits their ability to provide the data needed for operational decision-making.

This creates a critical gap where unsafe, contaminated, adulterated, or substandard pharmaceutical products may enter the U.S. supply chain before defects are identified. Product recalls and adverse event investigations can reduce harm after the fact, but often patient harm is the initiator of recall investigations and more are at risk before recalls can be fully implemented. In one case alone, recalls of key cardiovascular drugs over the last decade have affected 150 million patients on those medications. Problems can range from mislabeling, to potential cancer-causing contaminants, to deadly sterility issues. Additional scanning for quality and safety at importation or distribution could reduce or eliminate many of these recalls but the current technologies do not offer scalable preventive screening capabilities. ARPA-H is seeking solutions that can fundamentally improve the speed, scale, and operational utility of through-package pharmaceutical quality screening that can be deployed to ports and warehouses to prevent unsafe products from ever reaching patients.

2. PROGRAM OVERVIEW

The Fast Assessment of Solutions, Therapeutics, Pharmaceuticals, and SupplieS (FASTPASS) program seeks to develop and demonstrate a near real-time, non-destructive, through-packaging, AI/ML-enabled testing and quality monitoring system for pharmaceutical products and their inputs. The objective is to enable rapid, mobile, field-deployable screening of pharmaceutical products at import points of entry and other critical supply-chain nodes. The goal is to assure patients of the quality and safety of every dose of every medicine, every time.

FASTPASS is intended to augment, not replace, FDA inspections and existing manufacturing controls by adding a new layer of product-level quality intelligence. The program aims to combine multiple orthogonal sensing modalities with advanced machine learning, signal processing, and multi-modal data fusion to quickly identify contamination, adulteration, formulation variability, or other quality defects without opening packages or destroying products.

Success under FASTPASS will create a rugged, fieldable system operators can use to rapidly screen pharmaceuticals with extremely high specificity and throughput in real-world environments. If achieved, patients and clinicians will see the results of FASTPASS when it prevents recalls of unsafe products and drugs with high variability are identified more quickly.

Over time, this will also strengthen pharmaceutical supply chain resilience and provide a foundation for future use cases in related areas such as food safety, food contamination detection, and biothreat response.

3. FAST ASSESSMENT OF SOLUTIONS, THERAPEUTICS, PHARMACEUTICALS, AND

SUPPLIES (FASTPASS)

The FASTPASS program is soliciting proposals that radically develop or significantly advance through-packaging sensing technologies, combinations of multiple such technologies, and computational models to cut through noise and amplify hidden signals in order to achieve reliability and sample coverage far beyond what current technologies can attain. Lab-based tests already provide the gold standard for precision when samples can be crushed, dissolved, or otherwise prepared and flowed through the machines. However, destructive techniques, even many techniques requiring direct sample contact, are unsuitable for the high volumes, low residue containers, and variety of imported pharmaceutical products. Current non-contact detection technologies like Raman and hyperspectral imaging used in the food industry and drug manufacturing can discern chemical information, and some contaminant information at reasonably high throughput but in those instances they are not used through packaging.

Electromagnetic, optical, and other noise sources in a field environment can disrupt many spectroscopic techniques. Other technologies like nuclear magnetic resonance spectroscopy and x-ray diffraction can “see” through packaging but are typically used in controlled lab circumstances or with machines too big to be mobile. These and other technologies will need next generation upgrades to enable FASTPASS success.

International shipping requires many layers of packaging. FASTPASS will not focus on crates or cartons of product, but instead on the inner layers of packaging that maintain the product and label integrity. The program focuses on 6 major packaging types that make up the bulk of shipments for generic, biosimilar, and over the counter drugs and their ingredients.

• boxes of liquid vials or syringes

• bottles of solid oral dosage forms

• boxes of lyophilized vials

• boxed blister packs

• boxed liquid solutions in bags

• barrels of bulk powdered ingredients

Given the range of speeds, products, and difficult operational conditions, FASTPASS will require proposals and systems to be ambitious enough to meet these goals. Millions of pharmaceutical products enter U.S. ports daily. Fortunately, statistical sampling techniques mean that quality and safety assurance can be achieved by testing a small number of units from each shipment. Even so, analysis will still have to be reduced to approximately 15-20 seconds per sample.

Concurrently, false alarm rates must be extremely low to avoid burdening confirmatory analysis labs unnecessarily. The systems should also be designed to streamline operator input and decision-making to maintain high throughput. Finally, since operating ranges for temperature, humidity, vibration and other signal disruptions are higher than a typical laboratory, the system will have to self-calibrate and accommodate those conditions. Proposals must address integration of sensing and analytic components into a transportable, ruggedized, battery-capable or otherwise field-operable system suitable for deployment at import points of entry, warehouses, or other relevant operational nodes. For further guidance on technical needs, see the Metrics and Milestones section below.

TECHNICAL AREAS

FASTPASS begins with two technical areas (TAs) and ends only when they work harmoniously, together in the hands of port inspectors during demonstrations. The first TA focuses on individual sensor modalities. Each of these will have the ability to gather significant chemically specific information regarding the make up of a product without opening the package. Because it is unlikely that any one sensor modality will achieve the chemical specificity needed for conclusive pharmaceutical quality measurements, multiple modalities will be combined. The second TA focuses on using multiple sensor modalities harmoniously to reliably achieve specificity. While this primarily involves building machine learning models that interpret the multiple streams of sensor data, it incorporates other aspects of harmonious function. These include but are not limited to automation such as sample positioning or targeting, user interface(s), and leveraging synergies that may exist between sensing modalities.

TA 1: SENSING TECHNOLOGIES

TA1 performers will develop next generation through-packaging sensors. Work in TA1 will advance and integrate one or more non-destructive sensing modalities capable of interrogating pharmaceutical products. It will achieve greater signal clarity without opening packaging via technical improvement that may include but are not limited to leveraging emerging and tunable energy sources, signal offset methods, adaptive control algorithms, high gain antennae, and more. Ultimately, the techniques will be expected to collect usable data through some but not all commercial packaging materials including paperboard, plastic sheeting, white plastic, blister packs, glass, and related shipping materials.

FASTPASS technologies may include, but are not limited to, development or integration of methods such as nuclear magnetic/quadrupole resonance, spatially offset Raman spectroscopy, time-domain terahertz spectroscopy, x-ray fluorescence, x-ray diffraction, hyperspectral approaches, or other orthogonal, penetrating, non-destructive sensing techniques. ARPA-H is not prescribing one or more sensing method and is interested in:

1. Novel through-package sensing technologies

2. Revolutionary advances in capabilities of known technologies to increase resolution, penetrative performance, or meeting the need for mobile field operations

3. Novel system-level approaches that combine modalities to overcome the limitations of any individual technique.

For example, X-ray techniques have little difficulty penetrating typical pharmaceutical packaging, and instead would focus on meeting chemical specificity, regulatory, size, weight, and power metrics. Raman techniques are often used in manufacturing process controls, but may need to increase operational through-packaging thickness penetration and reduce matrix noise to obtain signals with usable limits of detection. To this end, proposers are expected to include their own additional aggressive metrics and milestones specific to their sensor technology.

As described above, measurement speed is critical and should be optimized along with specificity to maximize throughput without generating false alarms. The vast variety of packaging form factors will likely necessitate hand loading the samples. This suggests that full multi-modal analyses completed faster than ~10s will achieve diminishing returns in terms of operational throughput. However, alternatives to hand loading will be considered in proposals for particularly fast analyses, should hand loading create a bottleneck for throughput. The variety of sample types and packaging will mean that most sensors will have gaps in the types of products and packages they can analyze. It is then expected that TA2 performers will leverage data from at least two TA1 modalities of similar specificity to create compound or formulation specific “signatures” to support advanced analysis. This requires all TA1 technology data streams to be fully interoperable. Selection of TA1 sensor modalities will favor technologies capable of successfully analyzing a wider range of products and packaging, with some exceptions for technologies that fill specific but important gaps, such as sterile injectables.

TA 2: MULTI-MODAL MODELS

TA2 performers will develop advanced processing algorithms and models to distinguish drug quality and safety characteristics from real world data. Processing algorithms for low signal/high noise samples and deep learning models for classifying multi-modal data have advanced significantly in the last two years. FASTPASS requires analytics and models that enhance the sensor technological developments of TA1 so that they can fully realize the FASTPASS goals.

TA2 performers will be required to develop models that address one or more of four critical areas:

1. Adaptable frameworks for reliable self-calibration, protocol selection, and signal capture across variable scanning modalities, timing, and sample types under real-world conditions. For instance, statistical and AI-driven decisions for initialization parameters may significantly improve repeatability. These and other methods will automate system preparation and data collection from multiple TA1 sensor nodes.

2. Signal processing that incorporates chemometric and computational physics to inform spectroscopic analyses, reduce noise and identify signal components. Models will ingest multiple spectra from multiple modalities to form a clearer picture of what is being sampled. It is expected that these techniques will accelerate signal processing and improve fidelity.

3. Classification and categorization models and algorithms to increase specificity, improve consistency, and reduce noise across the range of samples, data streams, and filtered results produced by the system. Variability in many parts of the system will lead to small changes in signals, making classification more complicated. Deep learning and classification algorithms are expected to unlock chemical and product characterization for difficult signal sets.

4. Synthesizing signals from all available TA1 sensing modalities, accounting for real-world variability and uncertainty to fill gaps formed by individual sensor physical limitations.

Based on those limitations, each TA1 sensor modality will not be able to scan all potential packages and products. This modeling step is expected to significantly increase model confidence, reduce error rates, and improve traceability.

The system is expected to successfully process, identify, and enable decisions making the most of potentially weak, noisy, and heterogeneous signals, especially when comparing them against a growing range of reference standards or equivalent quality spectroscopic signatures. It is then expected that TA2 performers will leverage data from at least two modalities of similar specificity to create compound or formulation specific “signatures” to support advanced analysis. While TA2 focuses on building the models described above, it also includes all aspects of system software such as automated or guided sample positioning, leveraging synergistic aspects of detection modalities such as using imaging modalities to provide targeting for point detection modalities, managing instrument resources, timings, and user interface.

TEAMING:

Proposals are only required to address a single TA. However, an individual company, organization, or team can propose to both TA1 and TA2. TA1 proposals may address more than one sensing modality, but that is not required. TA2 proposals must ingest data from at least three sensing modalities, but these data sources do not need to come from TA1 performers (internal or partners) until after 18 months. TA1 performers with a particularly promising technology are permitted to propose independently, but it is highly encouraged to be partnered with at least one group addressing TA2. It is also encouraged that TA2 proposers seek out and consider partnering with TA1 proposers. A section in each proposal will list partners, the nature of the partnership, and the formality of the agreement.

Additional partnering agreements can be made and formalization of agreements can continue within the first phase (18 months) of FASTPASS. After 18 months, performers in both TAs will be down selected. It is anticipated that FASTPASS efforts beyond month 19 will consist of relatively few TA2 performers leveraging input from all remaining TA1 performers.

It is anticipated that only the best performing individual performers (as determined by ARPA-H) will continue into Phase II with others being down-selected within the first 18 months. Phase II work will be performed only by teams of successful Phase I performers, with the best team(s) (as determined by ARPA-H) proceeding to Phase III. Opportunities for re-teaming will be available to allow performers in Phase II and III to realign and ensure smooth integration of system components and models.

PROPOSER TEAM STRUCTURE:

Due to the need for inter-team cooperation in the FASTPASS program, offerors may choose to submit a proposal as a prime/sub-performer team or as a multi-party team. Offerors should identify the teaming arrangement most suitable to their specific combination of expertise, capabilities, and execution plan. In addition to inter-team collaboration, it is expected the project manager of each team will communicate consistently with the ARPA-H FASTPASS team to overcome technical hurdles and discuss progress.

Regardless of the selected teaming approach, offerors are required to submit one, singular proposal, per team that responds to any number of the requirements stipulated by this ISO.

PRIME/SUB TEAMING APPROACH:

The prime performer/sub-performer teaming arrangement is the “traditional” teaming structure approach. It is anticipated to be utilized by teams who wish to have a single prime performer be responsible for interactions with the government and sole responsibility for the performance of the project. The prime performer is responsible for all negotiations, managing the sub-performers, and addressing any administrative or performance issues. The prime has the sole responsibility for team management and program execution. In this teaming arrangement, leadership may not be changed for the full duration of the project and sub-performers do not have direct interaction with government partners on contract matters but may participate directly in technical meetings where their work is discussed. Privity of contract is solely between the government and the prime.

MULTI-PARTY TEAMING APPROACH:

The multi-party teaming arrangement can be useful for a group of organizations and/or performers working together to accomplish a common goal, with members sharing resources, knowledge, and expertise. While one team member is usually elected to serve as the lead member or authorized (human) agent for administrative purposes, such as executing documents or receiving payment on behalf of the team, each member must be bound to the team membership agreement and must be a party to the resultant OT award with ARPA-H. In this type of team structure, each member must perform substantive technical work as part of the team. This approach is also not exclusive to a single technology developer and a single product sponsor. Instead, it can involve larger groups of developers and sponsors who may have ready applications for the developed tools within their current development pipelines.

Unlike a prime/sub arrangement where the prime performer is the leader of the team throughout the duration of the project, the multi-party teaming structure allows different members of the team to take the lead role at different stages of the program life cycle based on expertise and experience, as needed. This approach supports collaboration among multiple developers and sponsors, enabling teams to pursue technology development, proof-of-concept, and adoption throughout the FASTPASS program. Additionally, the team structure allows for changes in team membership whenever necessary and enables new partners to join the team. The multi-party team arrangement is dynamic, allows for open communication between the government and all performers on a team, and ensures that all team members are responsible for performance and invested in the success of the program.

Teams are formalized through a teaming agreement (“articles of collaboration”) that outlines roles and responsibilities. The government is not a party to this agreement and does not dictate its terms. Team members decide how to manage membership and internal arrangements, including allowing members to join or leave as needed.

A multi-party team structure offers several advantages over a traditional prime/sub-performer model. It fosters collaboration, often leading to lasting partnerships, and gives the government direct contractual relationships with all team members. This structure provides the government with greater visibility, enables direct communication, ensures shared responsibility for performance, and allows for flexible leadership and membership as the project evolves.

If proposers decide to use a multi-party teaming approach, they are required to follow these minimum requirements:

1. Subcontracting is allowed only for non-essential work; all substantive contributors must be team members.

2. A team member must be designated to handle administrative functions and act as (human) agent or lead and must also contribute substantive technical work.

3. A fully executed teaming agreement outlining roles and responsibilities must be in place before award. All team members must be parties to the OT agreement. The lead performing organization must be able to change during the project if needed. The agreement must cover the full scope of the FASTPASS program. The government will not approve the agreement but will require proof that it has been executed by all team members before making an award.

ARPA-H recognizes that this approach may be unfamiliar or new to many proposers and strongly encourages proposers who are interested in a deeper explanation of this approach and how it can be fully utilized by teams to attend the FASTPASS Proposers’ Day and ask any questions.

DEMONSTRATING PROGRAM SUCCESS:

This program will be driven by technology demonstrations throughout each Phase. Initial milestones and demonstrations of sensing technologies and computational models will be of known or simplified samples under controlled conditions. Final demonstrations will be in real-world conditions with real-world samples. The goal of the program is to develop an integrated prototype system with proven performance capabilities, though without the final polish of a commercial system.

The prototypes will be expected to operate in typical port and warehouse settings that do not have specialized shielding, interference mitigation or infrastructure integration. The system must be able to achieve nominal operations using a combination of physical, electronic, and engineering controls to mitigate ambient interference.

Calibration of the system after moving it from one warehouse to another is a primary concern with field ruggedness. Similar systems are capable of being moved, but often require system experts to realign them. Each element of the FASTPASS system must be able to be transported from one location to another without recalibration or performing automatic recalibration.

Enabling automated sensing and calibration of the FASTPASS system is one of the primary shared goals of TA1 and TA2, including developing sensor packs feeding machine learning algorithms that can appropriately calibrate the systems for use with minimal intervention.

The entire FASTPASS system (comprising the different screening modalities, automation components, and software) will require individual components to be interoperable, either functioning in tandem or through automated transfer of samples from one screening modality to another.

Eventually, the systems should be highly reliable under their intended use conditions, with mean time between failure (MTBF) targets of 2000 hours for fully operational and tested systems. Test and pilot systems created for FASTPASS are not expected to meet this metric but to have this goal in mind for future commercialization efforts.

In its final stage, the system hardware and software should be able to accurately identify spurious results and system faults when they occur. Sensing and data collection that can be used to predict required system maintenance or system degradation events should be under development and testing by the end of the program.

Operator intervention should be minimized or eliminated for each unit operation in the system to complete all of its intended tasks. Training plans and materials should be developed and validated by the end of the program so that inspection staff at ports and warehouses are able to operate the system with a goal of greater than 95% success under nominal conditions when systems transition from prototypes to commercialization.

Full documentation of each sample, through visual imaging, machine readable codes (barcodes, QR codes) are expected to be integrated into the full FASTPASS system by the completion of the program.

SCHEDULE:

FASTPASS is a 54-month program structured to mature and integrate advanced through-packaging sensing modalities with AI-enabled decision support into a fieldable platform for pharmaceutical quality and safety screening at ports of entry. FASTPASS will have a phased program structure wherein the program is divided into three 18-month phases aligned to escalating performance expectations. FASTPASS goals are aggressive and only the best performers and teams will be selected to continue from one phase to the next.

Progress in FASTPASS will be primarily assessed at multiple demonstrations of component and system performance in increasingly realistic environments, including performance against representative packaging, environmental noise, throughput constraints, and other field conditions relevant to operational adoption. These demonstrations will increase in operational realism over the course of FASTPASS and will occur before the end of each phase and at roughly the midpoint of Phases I and II. In addition to phase-end, down-selection may also occur at any point during the program, such as due to poor performance at the midphase demonstrations.

It is expected that due to the aggressive goals of FASTPASS, developments made to technologies and models throughout each phase will advance the state of the art even during intermediate phases.

INDEPENDENT VERIFICATION AND VALIDATION (IV&V) AND OPERATIONAL PARTNERS:

Independent of this solicitation, ARPA-H will engage government partners during FASTPASS.

Operational Partners, such as from the Food and Drug Administration, may provide facility locations, where demonstrations would take place, as well as advice and feedback on their operational and technical needs. Demonstrations will therefore be orchestrated in a manner that minimizes or eliminates deleterious impact on their operations. IV&V partners will also be engaged to perform several independent analyses of performer progress:

• Specification Harmonization: Due to the highly integrated nature of this program, the IV&V partner will collect and maintain the user and system requirements for the final FASTPASS system. They will ensure that performers meet the requirements and that each element of the system is either fully integratable or interoperable with the others.

• Testing: IV&V will develop and implement testing samples and protocols, primarily for demonstrations but additional performance testing may be needed.

• Operational Readiness Analysis: In conjunction with Operational Partners, IV&V will evaluate components and prototypes for their utility to the Operational Partners and ability to function in the intended environments.

• Techno-Economic Analysis: With inputs from performers and industry analysis, IV&V will provide ARPA-H market analysis (E.g. estimates of production costs for FASTPASS product(s), market demand, etc.)

• Additionally, solutions and prototypes will be evaluated by a team of stakeholders and technical experts to assess their vulnerability to adversarial circumvention, corruption or spoofing of FASTPASS product(s).

Performers are expected to work with and provide information/data to IV&V partners to successfully accomplish these tasks.

MILESTONES AND METRICS:

The Metrics Table below lists aggressive target milestones to be achieved by and demonstrated in phase end demos. These metrics are representative of the program ambition and may be refined in proposals. Each technology and process will have distinct challenges to overcome that may not be listed below. Likewise, some metrics below may already be accomplished for certain technologies. Development that fills the gaps between technologies and raise overall performance will ultimately achieve the FASTPASS goals. Consequently, every proposal will not be expected to achieve all of these metrics, or these exact metrics, as part of their proposals.

Proposals are required to include relevant, and potentially additional, metrics and aggressive but feasible milestones showing rapid development of their particular technology toward FASTPASS goals. Performers should describe any expected applications that achieving intermediate milestones will unlock, throughout the program, before the final programmatic goals are reached. Achieving the overall program goals will require combining the sensing capabilities of multiple technologies (TA1) with the advanced processing models (TA2).

Therefore, Milestones should be developed within each team’s capabilities. If all metrics cannot be met by a single team, the milestones should include description and timing of partnerships or collaboration that will be needed to achieve the overall objectives.

Metrics will be achieved progressively through milestones that build upon each other. When describing milestones and deliverables, proposers should conform to the ARPA-H definitions of these terms. Milestones indicate the achievement of a scientific, technological, or performance breakthrough. These may be absolute (e.g. achieve resolution of 0.1mg/ml) especially at the beginning when baseline capabilities are being established. They may also be proportional (e.g.

achieve an increase in resolution of 25%) especially in the middle stages of the projects when baseline performance has been established and constant increases in technical capabilities over time are required to meet the final metrics. Deliverables are methods to show that milestones and progress have been achieved by providing the program team with data, demonstrations, reports, presentations, or by completing tests provided by the IV&V partner.

Phase 1 Phase 2 Phase 3

TA1 Metrics Early Integrated Prototype Final Prototype

Identify APIs >30% of formulation, contaminants > 5% by mass

APIs >10% of formulation, contaminants > 1% by mass

Size, Weight, and Power

N/A Volume ≤ 4m3; weight ≤ 120 kgs; Battery ≥ 6 hrs

Volume ≤ 2.5m3;

weight ≤ 70 kgs;

Battery ≥ 12 hrs

Scan time (s) ≤120 ≤30 ≤13

Scan Volume 10cm x 6cm x 6cm 20cm x 10cm x 10 cm 50cm x 50 cm x 40 cm

Package Type Coverage (Classes)

3 distinct 5 distinct 6 distinct and combinations

Time to Readiness

60 minutes, semi-automated

30 minutes, automated 5 minutes, automated

Penetration Through polypropylene (PPL) bag + 0.3mm paperboard

0.3mm PPL bottle + 1mm paperboard or 0.3mm paperboard with air gaps

PPL bottle + cardboard w/air gaps;

2mm cardboard drum capability

Environmental Robustness (Physical)

Mitigates most applicable interference by > 50%

Mitigates 2 most applicable interference by 75%

TA2 Metrics Early Integrated Prototype Final Prototype

Model Improved Signal to Noise Gain

5X 10x 20X

Time to Result ≤120 seconds ≤30 seconds ≤13 seconds

Improved ID Using multi-modal signals

2X Limit of Detection (LoD), asynchronous processing

5X (LoD), synchronous processing

10X (LoD), synchronous

Accommodation for ambient conditions (Digital)

N/A Automated mitigation of 2 major interference sources up to 75%

Automated mitigation of all interference sources up to 90%

Recalibration time

N/A 30 minutes 5 minutes

Formulation Quantification

Sense all differences > 40% from reference

Sense all differences >20% from reference

Sense all differences >10% from reference

Overall Metrics Early Integrated Prototype Final Prototype

Combined Specificity

95% 99.50% 99.99%

Minimal detectable substance

5% 0.5% 0.05%

OUT OF SCOPE PROPOSALS:

Technologies that require destructive preparations (e.g. mass spectrometry, liquid chromatography) are out of scope for this ISO, as are technologies that require opening sample containers to physically touch the product (e.g. dip tubes, liquid probes, microfluidics). Signal processing and models that merely apply existing methods or large language models to the FASTPASS input signals will likely not be innovative enough to be in scope for this ISO.

FASTPASS is also not intended to develop lab-based analytics, clinical diagnostics, manufacturing process analytical technologies (PAT), or data tools for electronic healthcare records.