I agree that pre-clinical work frequently entails a lot of moving parts before the "real science" ever begins, which makes the organizational and approval side seem like a road block. The conflict between thoroughness and quickness is what caught my attention in your statement. Businesses and companies must demonstrate progress rapidly, while contracts, protocol approvals, and animal studies don't really allow for shortcuts.

In my opinion, the transferability of pre-clinical findings is an additional difficulty. The concern of whether the results in controlled environments or animal models will truly predict safety and effectiveness in humans is still there even when the research is properly designed and carried out. Because of this uncertainty, teams must plan studies that are both scientific and carefully selected to maximize the amount of information that regulators will find significant.

The ethical aspect is another topic that is important: how can we plan studies to employ as few animals as possible while producing data that regulators and physicians will be able to rely on? One example is the drive toward in vitro models or organ-on-chip systems, although these technologies are still not yet able to fully replace conventional pre-clinical research.

So I feel like both the organizational aspect and scientific aspect equally play a big role and serve as a challenge together as we need to balance regulatory requirements, timelines, ethical considerations, and scientific validity all at once.

Looking ahead, do you think a day will ever come that pre-clinical development will shift more toward alternatives like AI modeling, or will animal studies continue to be the standard for regulatory approval?

I agree with how difficult the organizational and approval process is extremely time-consuming since, as stated in the lectures, lawyers take months to sign contracts, and things don't always go as planned. However, I think doing the actual research is a bigger challenge since getting results from basic science research takes a lot of time, and results don't always mean your research was successful. I currently work in an academic research lab, and almost every experiment takes days, sometimes weeks to finish. Additionally, at the end, the results could be contaminated or not be significant enough to move forward with. Basic science research moves extremely slowly, and it can be years before something substantial is discovered. However, I also believe that pre-clinical research in industry is different than basic science research for academia, as for industry, the research is mostly to confirm certain ideas and aspects of the device for safety before there is human testing. This would move the project forward faster. However, if there is a novel scientific question that needs to be answered, I believe the scientific aspect would be much more difficult to handle than the organizational, as the organizational aspect is being on top of your work and reaching out to lawyers frequently, which is a skill that can be learned from practice, but with the basic science aspect, the work becomes higher level and more difficult to just push through.  

With the integration of AI into pre-clinical research, I think both the organizational and scientific aspects can be sped up. With the organizational side, AI can get through requests much quicker and streamline different approval processes for the humans working in these departments. For the scientific side, AI can help model and predict the outcomes of experiments before they are performed, ensuring that you are not doing time-wasting experiments. To answer Shreya's question, I think AI modeling will become a part of research, but it will not completely take over animal testing for a very long time, since AI is still not 100% reliable, and ensuring the most reliability in an experiment with real-animal testing is crucial for safety. 

Do you think AI can be integrated into the organizational aspect of research, as I have mentioned above? Will there be new roadblocks with the integration of AI, and how much should AI be incorporated to protect privacy while enhancing speed? 

From my perspective, the biggest challenge in pre-clinical device development is navigating the organizational and approval process. Scientific questions are of course complex, but researchers are trained to handle uncertainty, run experiments, and adapt protocols. What often slows progress the most are the regulatory requirements, ethics approvals, and logistical hurdles that must be cleared before testing can even begin. For example, arranging contracts, securing animal or cadaver models, or ensuring compliance with FDA and institutional guidelines can take months, even before a single experiment is performed. This creates tension between the scientific timeline and business demands for speed. Unlike the science itself, which can be iteratively refined, these administrative steps are rigid and often outside of the researcher’s control. Solving the science is difficult but achievable within the research skill set. Overcoming the bureaucratic and regulatory barriers, while still ensuring patient safety and ethical responsibility, is what makes pre-clinical development challenging.

I think that what the tougher challenge is really depends on the on the complexity of the specific scientific questions that need to be solved, that will vary from study to study. The complexity of the organizational and approval process will vary depending on what kind of labs the study needs access too, as well as possibly the number of involved departments, and timeline reasons. However, I feel that the complexities of the scientific questions that need to be answered can be far more varied in how tough they are depending on how novel the issue is and if a similar study had been done by the company before. For example, a new medical device that is actually just an improvement on an existing device may need a small amount of new scientific questions that need to be answered but instead just repeat existing pre-clinical studies to account for new materials. However, the approval process may be just as time consuming if all the same approvals are necessary. In addition to this the pre-clinical testing might have to adhere to GLP practices or other regulatory issues. Compared to a novel medical device or a device that has many improvements and changes then the scientific questions may become more difficult to answer and be more work than the organizational and approval process.

I would definitely say from a researcher's perspective, navigating the organizational and approval process is the more challenging part of pre-clinical development. If you think about a researcher prior to joining a lab or committing to doing research, they will already be generally aware of the scientific questions and the challenges that come with trying to answer those questions. Although this is definitely a difficult aspect, what I would assume more researchers stress about than not is the organizational and approval process. Formulating ideas, organizing them into a proposal, and then trying to get those ideas approved typically takes long times, have harsh requirements, and guarantees nothing. For example, when going through the approval process you have to make sure your methods abide by regulatory standards (GLP), compliance with the FDA at times, university guidelines and practices, and if you are in industry, business guidelines and deadlines. Trying to manage this while having no guarantee of whether anything will get approved (especially for more extreme cases of research) makes it difficult to even get the research started in the first place. 

I also wanted to add that considering other cases is important. For example, @vanshamin mentioned how some scientific questions can be much more complex or in depth than others. I agree with his point because in industry, a lot of the times, the "research project" may not necessarily be formulating a new product but trying to fix or advance it. This may, in some cases, be easier than actually thinking of a problem and making a device from scratch. In the case where it is an improvement to an already existing product, I would think the organizational and approval process is a bit easier than trying to make the product. In the case where it is from scratch or a new product, I think approval especially would be more difficult since it might not be something that is related to anything on the market, and although you might be able to answer the scientific questions, getting it approved by other minds might not be as easy. However, in general it could vary case to case, but I believe in most cases the organizational and approval process is generally the more stressful and more difficult part.