1. To start, could you please introduce yourself and tell us a bit about your educational background? What are you currently working on or involved with now?
My name is Ramisa Fariha; I was born and raised in a small town called Narayanganj in Bangladesh. I came to the United States in 2013 to pursue my Bachelor of Science in Biomedical Engineering at Penn State University. Upon graduation, I joined ACell Inc. (now a part of Integra LifeSciences) where I worked for a year in R&D, before starting graduate school.
I attended Brown University from 2018-2020 for my Master of Science in Biomedical Engineering, working at the Lee Lab for Biomedical Optics and Morgan Lab for Tissue Engineering. In 2020, I graduated and was recruited by Dr. Anubhav Tripathi to join his lab at Brown University for my Doctor of Philosophy in Biomedical Engineering. I made a significant shift from by background in cell and tissue engineering and delved into analytical chemistry, automation, and device design. That is where I learned the most and conducted the majority of my translational work.
I graduated in 2024 and joined the Brown University RNA Centre as a postdoctoral researcher. I have led the establishment of the mass spectrometry core at the Centre, and am responsible for developing workflows for the biobanking of tRNA modifications occurring in different species under various conditions.
2. What led you to focus on opioid testing as part of your work? Was there a specific moment or motivation behind it?
Great question! During my Ph.D., we collaborated with several academic and industry partners. One of our collaborators was Dr. Carolina L. Haass-Koffler, PharmD., PhD, Associate Professor at Brown University Department of Psychiatry and Human Behaviour, Behavioural and Social Sciences, Centre for Alcohol and Addiction Studies, and the Carney Institute for Brain Science. She was leading a human laboratory study investigating the impact of oxytocin administration in individuals with opioid use disorder.
As we became more involved in her work, we saw the limitations in current sample collection methods, particularly when working with human subjects. This sparked the idea that we could contribute by developing microsample-based assays to improve efficiency and feasibility. For the adult human laboratory studies, we created a serum-based assay requiring only 20 microlitres of samples for a fully automated, tier-2 multiplex assay for opioid quantification.
Around the same time, we discovered a striking gap in neonatal care. There is currently no absolute quantitative method to detect Neonatal Abstinence Syndrome (NAS), a condition affecting infants exposed to opioids in utero. Incredibly, most diagnosis are still made using symptom-based questionnaires! In the US, dried blood spots (DBS) are routinely collected for newborn screening, but the sample volume is very limited (only 4-5 spots per infant are available for all the tests), and to develop a reliable quantitative technique for NAS means working with the smallest possible sample, with a fast turnaround time.
To address this challenge, I developed a method using just 3 mm sub-punch from a DBS (around 3 microlitres of blood) with a focus on rapid turnaround. We then fully automated the workflow. While my team had automated several assays before, this was our first using DBS with such low sample volume and speed requirements.
3. Can you briefly explain what your team has developed and why it’s significant in addressing the opioid crisis?
So previously, we had showcased the use of an electric field for steroid extraction from DBS for potentially early screening of PCOS in teens (Fariha et al. 2024). We didn’t have sample limitations; we were only working to mitigate an accessibility issue. However, this time we had work with limited samples and the need for a quick and reliable quantification workflow.
As a result, we continued to work with John Murphy, an engineer at the Brown Biomed Workshop, to think about how to make this workflow completely automated, requiring minimal human interaction. This process involved multiple simulations and prototype tests, even during the Christmas holidays, and ultimately resulted in the development of our final device.
We developed a 96-well array device, that can hold pre-punched DBS (part of routine workflow) and utilises a hollow top-electrode. As a result, the entire assay is automatable on any robotic liquid handling platform. The circuit is not completed until the extraction solvent is pipetted into the device, and it only takes three minutes of electric field across the samples to extract the analytes, as opposed to multi-day standard workflows usually required for efficient analyte extraction from DBS samples.
Because the sample requirement is so small, healthcare providers will still have plenty of samples left to perform their routine tests and only need a minimal microsample for accurate opioid quantification in a fast and robust workflow. This will enable more targeted treatment of the patients. This is also applicable for adult specimens. Our workflow for the opioids is just one of the many use cases this device can have regarding DBS application. It can also be extended to steroids, vitamins, and even nucleoside analysis.
4. Your method uses just a drop of blood to detect multiple opioids. How does it work, and why is that an important breakthrough?
It is fast, hands-free, and compatible with robotic liquid handlers already used in clinical laboratories. The entire extraction process takes just three minutes, which is significant improvement over standard workflows that often take hours or even days.
The core principle is electric-field-assisted extraction. We apply a short electric pulse to the blood spot, which accelerates the release of opioid compounds into the extraction solvent. This dramatically reducing processing time without compromising precision.
This is a breakthrough because it automates the most labour-intensive part of mass spectrometry workflows, making high-complexity assays more compatible with real-world clinical timelines. You do not need a large sample, specialised handling, or extend processing time.
What makes this significant is the combination of minimal sample input, rapid turnaround, and scalability - all within a format that integrates into existing laboratory infrastructures. It enables accurate opioid quantification from the smallest samples available, without disrupting routine testing.
5. What were some of the biggest technical or scientific challenges you faced while developing these tests?
Working with such tiny samples was a major challenge, not just from a design perspective, but also in validating that we could extract enough analytes for accurate quantification and reproducibility. We also had to design a device that was automation-ready, biocompatible, and chemically inert, which required dozens of iterations and extensive testing.
Finally, integrating electric field dynamics with fluidic control across a 96-well format introduced significant engineering complexity. Every detail, from solvent delivery to voltage timing, had to be precisely calibrated to ensure reliable and efficient performance.
6. What are the key advantages of your new testing method over traditional blood or urine tests when it comes to caring for patients with opioid use disorder?
Urine tests are common, but they are not always reliable, especially in perinatal or neonatal cases. We have highlighted this in our Scientific Reports study, wherein the human subjects tested negative for opioid use via urine tests, yet we detected morphine and other drugs in their serum specimens using our testing protocol (Fariha et al., 2025). This is critical for human laboratory studies, where participants are often recruited with minimal screening and reliance on self- reported use of opioid. Since these studies aim to drive scientific breakthroughs, accuracy and precision are essential.
Blood testing is more accurate, but traditional assays require large volumes and involve lengthy preparation times. In speaking with our collaborators, we identified how challenging it can be to collect ‘sufficient’ samples for the human subjects to enable multivariable studies.
Our method requires either 20 microlitres of serum (for adults) or a sub-punch from a dried blood spot, and the sample preparation is completed within three minutes, followed by another three minutes of analytical quantification via LC-MS/MS. It reduces sample burden, eliminates manual preparation, and delivers a high-fidelity, quantitative readout, even in resource-limited settings. That is a game-changer for both adults and infants.
7. Your second study focuses on newborns. Why is neonatal opioid exposure difficult to diagnose, and how does your method help?
Diagnosing NAS is challenging because it is usually based on observing symptoms, which can be delayed, subtle, or easily mistaken for other conditions. At present there is no widely used biological confirmation method. Our tool gives clinicians a rapid, objective way to confirm opioid exposure - using a sample that is already collected at birth. It complements symptom scoring with concrete data, which can help guide treatment and ensure that opioid-exposed newborns are identified and supported early.
What surprised me was the current treatment approach for NAS patients- newborns are injected with high doses of morphine if they reach a certain number on the FNASS scale. Our method can be incorporated at state-level as it is, to begin collecting data on the baseline levels of various opioids in infant. This, in turn, more targeted and accurate treatment.
8. You’ve talked about the importance of designing tools that work “at the point of care.” What does that mean to you?
It means designing tools that work where the patient is, not just where the lab is. It’s about removing barriers between diagnosis and decision-making. Whether it’s a newborn in the NICU or a rural clinic with limited infrastructure, we need diagnostic platforms that meet people where they are, with speed and precision.
That said, I am also an advocate for accurate and reliable diagnosis. Hence, I am passionate about the widespread use of DBS as a sample source because it has the potential to eliminate the need for centralised and specialised labs. These samples are easy to handle - minimally invasive and requiring no refrigeration, so they can be accessed from all over the world, allowing everyone to receive the same quality of disease diagnosis.
This is particularly important when considering drug discovery and development. Having samples from all over the globe means that therapies will be developed based on a diverse range of data sets, rather than from a single demographic.
9. This project combined public health and engineering. How did that interdisciplinary collaboration shape the outcome?
Public health gave us the “why”; engineering gave us the “how.” Understanding clinical constraints such as sample limitations, turnaround times, diagnostic ambiguity - helped us define the problem beyond a purely technical exercise. This grounding shaped every design decision, making the work more meaningful and more real. I strongly believe the key to good engineering is understanding the need first.
10. How do you envision these diagnostic tools being used in real clinical settings, both in the U.S. and globally?
In the U.S., this technology could be integrated into newborn screening programs or specialty labs working on opioid-related care. Since samples are already being collected, it would be a simple step to add this test into the routine newborn screening to start gathering data and establish baseline for more quantitative treatment.
Globally, it opens doors for high-precision testing in low-resource settings where cold chain logistics, sample volume, or manual labour is a challenge. As long as a laboratory is interested in analysing a particular biomarker or analyte, whether opioid, steroids, or even transcriptomics information, the ability to use tiny sample volumes and increase sample accessibility allows for more diverse and inclusive scientific discovery.
11. As someone with roots in Bangladesh, how has your personal background influenced your vision for accessible healthcare solutions?
For the longest time, I was unaware that scientific discovery could be ‘made for the poor’. I had worked in high-tech labs, trying to answer questions that would not reach the people of Bangladesh for decades. It was when my mom asked me whether my science was only accessible to the rich that I began rethinking my approach. It also helped that my PhD. advisor Dr. Tripathi, share the same passion for low-cost and effective innovation aimed at equity.
Working in the Tripathi lab has helped me reframe my vision for global healthcare. I come from a place where diagnostics are not always guaranteed - where timing, infrastructure, and cost can delay care. Whether it is democratising the field of diagnosis via microsampling, or building tools that work in the real, messy global setting, my vision is a world where everyone has access to quality diagnosis, and ideally, early disease detection.
12. Finally, what advice would you give to young aspiring researchers and also those already working in the field who want their work to create real-world impact?
To young aspiring scientists: You are never ‘too young’ to make an impact. Our world today is much better connected than it was 10 or 20 years ago. If you have a vision, start reaching out and make connections. Dream it and just go for it!
For existing researchers: Even though high-impact publishing might be considered ‘the name of the game’, always take time to ask yourself who is your work truly impacting. Significant gaps in public health remain, weather developed nations or underdeveloped nations. Pause to understand lived experiences, and build solutions that can have an immediate and meaningful impact on the world.
The editorial team would like to thank Dr. Ramisa Fariha for her time and contributions.
References
Fariha, R., Rothkopf, E., Haass-Koffler, C.L. & Tripathi, A. , 2025. Opioid quantification via microsampling techniques to assess opioid use in human laboratory studies. Scientific Reports, 15, p.17678. https://doi.org/10.1038/s41598-025-99130-5
Fariha, R., Murphy, J., Walters, N., Rothkopf, E., Okoh, O. D., Lawandy, N. M. & Tripathi, A., 2025. Precision through electric‑field assisted automatable high throughput sample preparation of dried blood spots for neonatal abstinence syndrome detection. SLAS Technology, 32(1), p.100282. https://doi.org/10.1016/j.slast.2025.100282
Fariha, R., Rothkopf, E., Murphy, J. et al. 2024. Electric field-assisted dried blood spot sample preparation for analysis of steroids using LC–MS/MS. Advances in Sample Preparation, (Vol. 10). https://www-sciencedirect-com.revproxy.brown.edu/science/article/pii/S2772582024000147
National Institute on Drug Abuse. (2024, November 22). Opioids. https://nida.nih.gov/research-topics/opioids