Clinical Trials Data Management Systems What A Project: Fill & Download for Free

In this technocracy dominated era, there's no dearth of technological approaches to improve clinical trial (CT) participation. Ranging from alert systems linked to electronic health records (1) to online registries (2, 3, 4), the usual technology-based suspects have made their appearance on the scene to no avail. For e.g., available free to anyone online, ResearchMatch allows any US resident to register as a potential CT participant (volunteer). Hosted at Vanderbilt University and funded by the NIH, this registry launched in December 2008. Yet >7 years on, its name recognition is limited and <100000 volunteers have signed up (2).Obviously, core of the problem requires a human, not technological, touch. Meantime, the general population gains the bulk of its knowledge about CTs from entertainment fare online or on TV and from news (5, 6, 7, 8). Obviously such sources are more likely to fuel and sustain misconceptions rather than anything remotely close to the truth about CTs. Essentially, the current CT ecosystem woefully under-utilizes two of its foundational pillars,1) Referring physicians and other healthcare providers who lead patients to CTs (9, 10).2) Current and previous CT participants, i.e., potential Patient Advocates.Even more inexplicably, pertinent questions relating to the CT process remain unansweredWhat's the difference between healthcare providers who either do or don't participate in CTs, and between those who either do or don't refer patients to CTs?What are the recruiting strategies used by successful CTs (11, 12, 13)?Why don't organizers of successful CTs routinely record and report their recruiting strategies? Clearly trial funders should mandate their doing so.What level of engagement remains with CT participants after a trial's over? Do trial organizers and their staff stay in touch with them? While volunteers are enrolled in a trial, which can be for several months to even years, do trial organizers develop a rapport with at least their most enthusiastic participants, and teach and encourage them to advocate and recruit newer volunteers on their behalf within their families and communities (14, 15)? Given the current state of affairs, clearly not and yet wouldn't doing so set up a virtuous positively reinforcing cycle leading to cumulatively increasing CT participants? Instead, why is the system set up to recruit and forget once the trial's over? Isn't this an egregious example of re-inventing the wheel every time?~70 years since the 1st double-blind, placebo-controlled randomized CT and with >210,000 ongoing registered CTs across the US and 193 other countries (see figure below from 16), it's scarcely believable but sadly true that such basic issues aren't well-studied nor their lessons freely available for others' benefit (17, 18).Upon reflection, it's only to be expected that an inherently top-down and paternalistic enterprise like human biomedical research would under-utilize Patient Advocates. After all so insular is it that its very basics such as research ethics and regulatory oversight have been developed without seeking and incorporating the input of research volunteers (19), who are more frequently described condescendingly as subjects. Even peer-reviewed literature about woefully lacking CT participation rates is dominated by the voices of biomedical research aficionados. Where are the voices of CT participants? Why don't medical and scientific journals report their perspective, about their experiences and suggestions in their own words? Imbalance couldn't get starker than this (20). When was the last time the US FDA or the NIH convened meetings or town halls specifically inviting volunteer input into the CT process? Never. The current CT world is strikingly insular (21, 22).'Findings concur with previous research suggesting that CT investigators rarely communicate about clinical research outside of specific, study-based recruitment messages, which are often only provided to current patients already familiar with the medical institution...Findings from the current study, however, show that CT teams rarely promote CT research outside of the medical setting or reach out to community organizations to serve as an important conduit between the medical institution ß and hard-to-reach populations...Although investigators rely heavily on local physicians to recruit patients into their studies, there may be limited communication between the investigators and local physicians [37] and between these local doctors and their patients [28].'(8).The funders and fund recipients, i.e., clinical researchers and their support staff working largely in academic medical centers, currently control the process. They hold endless rounds of meetings and write exhaustive white papers and reports filled with earnest recommendations. These current CT stakeholders haven't yet thought to expand their fold and bring into it the ones whose voices perhaps matter the most in CT participation and logistics, patients and volunteers who've participated in CTs, i.e., Patient Advocates. We all know new drugs and therapies can't get approved unless robustly tested on large pools of volunteers, and yet those same volunteers, the very heart of human biomedical research, have no say in how the process could be structured so their ranks stay filled, not depleted.What Factors Deter CT Participation And How They Could Be MitigatedObvious ones are fears about unapproved medications and procedures, i.e., that one could be used as a 'guinea pig', as well as fears of side-effects, and that one could get a placebo instead of Rx due to randomization. Given such fears are likely pervasive among the population at large (23, 24, 25, 26), who could be more persuasive in convincing others to participate in CTs than those who've done so themselves? If previous trial participants aren't doing so, maybe there's something inherently discouraging about the process that urgently needs to be overhauled? While the medical and scientific aspects of CTs are rightfully the purview of clinical researchers and scientists, and should remain so, these patient-centric aspects are areas where Patient Advocates could help reshape the process to encourage others.Studies also suggest local community-based sources of CT information are seen as more trustworthy. These include local doctors, TV and community health centers (7). As well, informal family and community networks, i.e., family and friends, and local church and faith-based organizations (26).Cancer Clinical Trial (CT) Participation Rates Are High In Children Regardless of Race/Ethnicity But Very Low Among Adults. What Accounts For Such A Difference?Poorly envisaged top-down policies often lack mechanisms to enforce their recommendations. In US biomedical research, one of the most prominent examples is the 1993 NIH Revitalization Act that mandates inclusion of racial and ethnic minorities in federally funded biomedical research (27). 23 years on, African Americans and Hispanics represent 12% and 16%, respectively, of the US population and yet constitute only 5% and 1% of CT participants (28) while whites are over-represented (29). Why is this so? Among US CT volunteers, blacks are supposed to mistrust medical research, and language and culture are supposed to be barriers to Hispanic participation while implicit bias among clinical researchers is supposed to disfavor minority participation in CTs. However, a crucial piece of data unerringly rebuts these oft-repeated myths because there's more than adequate participation among children regardless of race/ethnicity compared to dismal rates among adults.In the US, only 3 to 5% of ~10 million adults with cancer participate in CTs (30). However, CT enrollment among <15 years old is anything but dismal. In the US, 60% of cancer patients aged <15 years are enrolled in CTs (31). That's not all. Proportion of minority pediatric cancer patients enrolled in cancer CTs (~10% blacks, ~12% Hispanic) ~matches their proportion in the population (32). This means neither do pediatric minorities systematically lack access to health research nor face systemic bias against CT enrollment. How to explain this huge difference between children and adult CT enrollment rates? What's different about the pediatric CT recruitment process? Undoubtedly, applying what works in recruiting children to CTs would hugely improve adult enrollment rates.Crux Of The Problem: Huge Gap Between Eligible And Actual Adult Clinical Trial (CT) ParticipantsReal gap in adult CT enrollment is ~10X. For e.g., in the US, ~20% of cancer patients are typically eligible to participate (33, 34) but only 3 to 5% of them do so (30). This huge gap between eligible and actual participants is the critical problem needing to be solved. Weakest link in the chain? Extremely poor inclusion of referring physicians and Patient Advocates into the CT recruitment process, i.e., we're back to square one, the need to expand the fold of current CT stakeholders to include patients and volunteers, and their physicians, and seek their input in improving CT participation and logistics. One approach could be to have CT participants access trial-related procedures and services closer to their home rather than exclusively at academic CT sites, which are often far from their homes.Clinical Trial (CT) Location Matters Hugely To CT ParticipantsTravel distance to and lack of transportation to and from the trial site are major barriers in CT participation (35, 36, 37). Even in the US, arguably the wealthiest country in the world and unquestionable global CT leader, many if not most CT volunteers need to drive >1 hour each way to reach a CT site (see figure below from 38).Bibliography1. Embi, Peter J., et al. "Effect of a clinical trial alert system on physician participation in trial recruitment." Archives of Internal Medicine 165.19 (2005): 2272-2277. https://www.researchgate.net/profile/C_Harris/publication/7519692_Effect_of_a_Clinical_Trial_Alert_System_on_Physician_Participation_in_Trial_Recruitment/links/00b7d51e41b31eddbe000000.pdf2. Harris, Paul A., et al. "ResearchMatch: a national registry to recruit volunteers for clinical research." Academic medicine: journal of the Association of American Medical Colleges 87.1 (2012): 66. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3688834/pdf/nihms335956.pdf3. Denicoff, Andrea M., et al. "The National Cancer Institute–American Society of Clinical Oncology Cancer Trial Accrual Symposium: Summary and Recommendations." Journal of Oncology Practice 9.6 (2013): 267-276. Summary and Recommendations4. Tan, Meng H., Matthew Thomas, and Mark P. MacEachern. "Using registries to recruit subjects for clinical trials." Contemporary clinical trials 41 (2015): 31-38.5. Kelch, Robert P. "Maintaining the public trust in clinical research." The New England journal of medicine 346.4 (2002): 285.6. Len-Rios, Maria E., and Qi Qiu. "Negative articles predict clinical trial reluctance." Newspaper Research Journal 28.1 (2007): 24.7. Tanner, Andrea, et al. "Communicating Effectively About Clinical Trials With African American Communities A Comparison of African American and White Information Sources and Needs." Health Promotion Practice (2015): 1524839915621545.8. Tanner, Andrea, et al. "Promoting clinical research to medically underserved communities: Current practices and perceptions about clinical trial recruiting strategies." Contemporary clinical trials 41 (2015): 39-44.9. Baer, Allison R., et al. "Engaging referring physicians in the clinical trial process." Journal of Oncology Practice 8.1 (2012): e8-e10. Engaging Referring Physicians in the Clinical Trial Process10. Robinson, M. Koa, JoAnn U. Tsark, and Kathryn L. Braun. "Increasing primary care physician support for and promotion of cancer clinical trials." Hawai'i Journal of Medicine & Public Health 73.3 (2014): 84. http://www.hjmph.org/HJMPH_Mar14.pdf#page=1211. Lai, Gabriel Y., et al. "Effectiveness of strategies to recruit underrepresented populations into cancer clinical trials." Clinical Trials 3.2 (2006): 133-141. Effectiveness of strategies to recruit underrepresented populations into cancer clinical trials12. Friedman, Daniela B., et al. "How are we communicating about clinical trials?: an assessment of the content and readability of recruitment resources." Contemporary clinical trials 38.2 (2014): 275-283.13. Friedman, Daniela B., et al. "A qualitative study of recruitment barriers, motivators, and community-based strategies for increasing clinical trials participation among rural and urban populations." American Journal of Health Promotion 29.5 (2015): 332-338. https://www.researchgate.net/profile/Caroline_Bergeron2/publication/261137533_A_Qualitative_Study_of_Recruitment_Barriers_Motivators_and_Community-Based_Strategies_for_Increasing_Clinical_Trials_Participation_Among_Rural_and_Urban_Populations/links/54c78b270cf238bb7d0ab8ab.pdf14. Friedman, Daniela B., et al. "Improving our messages about research participation: a community-engaged approach to increasing clinical trial literacy." Clinical Investigation 4.10 (2014): 869-872. http://www.future-science.com/doi/pdf/10.4155/cli.14.8715. Tanner, Andrea, et al. "Barriers to medical research participation as perceived by clinical trial investigators: communicating with rural and African American communities." Journal of health communication 20.1 (2015): 88-96.16. Trends, Charts, and Maps17. Michaels, Margo, et al. "Impact of Primary Care Provider Knowledge, Attitudes, and Beliefs about Cancer Clinical Trials: Implications for Referral, Education and Advocacy." Journal of Cancer Education 30.1 (2015): 152-157.18. Sriphanlop, Pathu, et al. "New York state health care provider participation in clinical trials: a brief report." (2016). http://www.vipoa.org/journals/pdf/9370870213.pdf19. Dresser, Rebecca. "What Subjects Teach: The Everyday Ethics of Human Research." Wake Forest Law Review 50 (2015): 301. What Subjects Teach: The Everyday Ethics of Human Research20. Holzer, Jessica K., Lauren Ellis, and Maria W. Merritt. "Why We Need Community Engagement in Medical Research." Journal of Investigative Medicine 62.6 (2014): 851-855.21. Comis, R. L., et al. "Baseline study of patient accrual onto publicly sponsored US Cancer Clinical Trials: an analysis conducted for the global access project of the National Patient Advocate Foundation." Philadelphia, PA, Coalition of Cancer Cooperative Groups (2006): 1-52.22. Friedman, Daniela B., et al. "What do people really know and think about clinical trials? A comparison of rural and urban communities in the South." Journal of community health 38.4 (2013): 642-651.23. Meropol, Neal J., et al. "Barriers to clinical trial participation as perceived by oncologists and patients." Journal of the National Comprehensive Cancer Network 5.8 (2007): 753-762. Barriers to Clinical Trial Participation as Perceived by Oncologists and Patients24. Weckstein, Douglas J., et al. "Assessment of perceived cost to the patient and other barriers to clinical trial participation." Journal of Oncology Practice 7.5 (2011): 330-333. http://nnecos.org/Resources/Documents/JOP-2011-Weckstein-330-3.pdf25. Fleisher, Linda, et al. "Application of best practice approaches for designing decision support tools: the preparatory education about clinical trials (PRE-ACT) study." Patient education and counseling 96.1 (2014): 63-71. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4171039/pdf/nihms621149.pdf26. Bell, Jennifer AH, and Lynda G. Balneaves. "Cancer patient decision making related to clinical trial participation: an integrative review with implications for patients’ relational autonomy." Supportive Care in Cancer 23.4 (2015): 1169-1196.27. Chen, Moon S., et al. "Twenty years post‐NIH Revitalization Act: Enhancing minority participation in clinical trials (EMPaCT): Laying the groundwork for improving minority clinical trial accrual." Cancer 120.S7 (2014): 1091-1096. Twenty years post-NIH Revitalization Act: Enhancing minority participation in clinical trials (EMPaCT): Laying the groundwork for improving minority clinical trial accrual - Chen - 2014 - Cancer - Wiley Online Library28. Clinical Trials Shed Light on Minority Health. FDA, April 2013. http://www.fda.gov/downloads/ForConsumers/ConsumerUpdates/UCM349488.pdf29. ThinkProgress, Tara Culp-Ressler, April 4, 2014. There Are Too Many White People In Clinical Trials, And It’s A Bigger Problem Than You Think30. Murthy, Vivek H., Harlan M. Krumholz, and Cary P. Gross. "Participation in cancer clinical trials: race-, sex-, and age-based disparities." Jama 291.22 (2004): 2720-2726. https://www.researchgate.net/profile/Cary_Gross2/publication/8520126_Participation_in_Cancer_Clinical_Trials_Race-_Sex-_and_Age-Based_Disparities/links/5471f6ea0cf24af340c3e241.pdf31. Fern, Lorna A., and Jeremy S. Whelan. "Recruitment of adolescents and young adults to cancer clinical trials—international comparisons, barriers, and implications." Seminars in oncology. Vol. 37. No. 2. WB Saunders, 2010.32. Bleyer, W. Archie, et al. "Equal participation of minority patients in US national pediatric cancer clinical trials." Journal of pediatric hematology/oncology 19.5 (1997): 423-427.33. Sateren, Warren B., et al. "How sociodemographics, presence of oncology specialists, and hospital cancer programs affect accrual to cancer treatment trials." Journal of Clinical Oncology 20.8 (2002): 2109-2117.34. Brawley, Otis W. "The study of accrual to clinical trials: Can we learn from studying who enters our studies?." Journal of Clinical Oncology 22.11 (2004): 2039-2040. Can We Learn From Studying Who Enters Our Studies?35. Kanarek, Norma F., et al. "Geographic proximity and racial disparities in cancer clinical trial participation." Journal of the National Comprehensive Cancer Network 8.12 (2010): 1343-1351. Geographic Proximity and Racial Disparities in Cancer Clinical Trial Participation36. Coakley, Meghan, et al. "Dialogues on diversifying clinical trials: successful strategies for engaging women and minorities in clinical trials." Journal of women's health 21.7 (2012): 713-716. http://online.liebertpub.com/doi/pdf/10.1089/jwh.2012.373337. Itty, Tracy Line, Felicia Schanche Hodge, and Fernando Martinez. "Shared and unshared barriers to cancer symptom management among urban and rural American Indians." The Journal of Rural Health 30.2 (2014): 206-213. https://www.researchgate.net/profile/Felicia_Hodge/publication/261289550_Shared_and_Unshared_Barriers_to_Cancer_Symptom_Management_Among_Urban_and_Rural_American_Indians/links/543556e30cf2dc341db0a397.pdf38. Galsky, Matthew D., et al. "Geographic Accessibility to Clinical Trials for Advanced Cancer in the United States." JAMA internal medicine 175.2 (2015): 293-295. Accessibility to US Clinical Trials for CancerThanks for the A2A, Joseph Philleo.

I get lot of queries and questions on being a data analyst, hence today I will explain my job profile in detail. This will be a long answer as I will try to cover various aspects of the work I do.Data analyst is of various kinds-Business Data Analyst, Clinical Data Analyst, Market Analyst, etc. Depending on the field of data, the name differs. Also, the methods, tools, software used vary so it is a vast field.I am an experienced Clinical data analyst. I will complete my 4 years in Clinical Data Management this November 2019. I have switched one company till date. My previous and my current employer, both feature in the world top 10 best companies. I also have 3 Barnett certifications.I had joined my previous company as a fresher and was trained for 2 months by the client, post that I had On Job Training and that is how I became a CDA. I did not study Clinical Research, I was trained by the company.Clinical Research is a vast domain where research for new drugs is carried out by the big pharma companies. It is outsourced to Clinical Research Organizations mostly. So, our clients or sponsors would be any company whose medicines are sold in the chemist shop.I have worked for several clients, been a part of successful drugs that have released in the market. So, yes we help bring out cure to diseases and that is based on the quality we deliver. We handle patient data, we are into human data science.There are many departments in Clinical Research- Medical writing, Pharmacovigilance, Data Management, Regulatory affairs, SAS programmers, Data base programmers, Data scientists, etc. I will only focus on Data Management as it is very vast.Data analysis involves the following:1. Data collection2. Data cleaning3. Data representation4. Data analysisSo, we get projects from sponsor for a particular therapeutic area- Cardiovascular, pediatrics, oncology, auto immune etc.Based on the protocol, the data base is designed by data base programmers. We have SAS programmers to help us catch discrepancies in data based on Data Management Plan which has all details of data restrictions to be applied. Data after cleaning is represented as required by the Biostasticians, mainly as TFLs (Tables, Flow chart, Listing) and is submitted to FDA for analysis. Finally, the drug gets rejected or is approved.Data collection is carried out by medical centers worldwide. Data cleaning is done by us, Data Analysts, represented by the Bios team and analysed finally by Scientists ,etc for quality and reliability.There are many other teams working closely in this big process.As a data analyst, one should be through with the ICH guidelines, GCP and SOPs. Data is collected via EDC (Electronic Data Capture) so we need to be familiar with databases. There are many- Oracle Clinical, Inform , Rave, in-house data base etc which one must be through with.Firstly, during the set up the database, testing and QC is performed by the CDA, it is the setup phase. Below will be performed by the CDA:1. Test case writing2. TestingOnce, set up phase is over, the trial starts and data is collected which requires daily and monthly cleaning via various activities, namely:1. Query management(system and manual checks firing in data base)2. Vendor data Reconciliation3. SAE Reconciliation4. SAS Listing Output5. PD Reconciliation6. Trend analysis, if any7. Data Set review, etcEach activity is different and cleans data from various aspects.Lastly, when a projects ends, a CDA is suppose to perform data base lock activities (review activities). Finally, data base is manually locked each form wise by CDA or script run locked by database programmers.A CDA is expected to be highly skilled in Excel as data analysis can be faster. Understanding of protocol, its deviations and study design, understanding the restriction criteria is also very important. Softwares are used, for certain output. Applications are used for report pulling etc.Also, a CDA should know metrics and reporting for knowing the status of the study. Interim analysis, futility analysis, data base lock are milestones which demands clean data so quality should be always maintained.Quality cannot be compromised as its live data so expectations are quite high. With proper training and mentoring one can be a good data analyst. As time passes, speed of analysing data also increases so it gets better with time.It’s a very comfortable job, can be done from home too so may become home-based later and shift to any desired place where net connectivity is good.P.S-I hope the information is helpful. Please do not ask me about Market and Business Data Analysis. I also cannot suggest you any institutions for Clinical Research, as my Master is on Microbiology.

As a person who has made drugs and is planning on making a career out of it, I hope that a lot of things change by the time I reach the middle of my career. The ideal drug development process would and should look very different from the current system in place.The interesting thing is that most of the interactions that already exists should stay in place. A large problem with the current inefficiency of the drug discovery / development process is that the incentives and goals are misaligned for all of the individual players in the drug making process. It's certainly an acknowledged problem and everyone in the drug industry talks about it. To describe how deep of a hole we're in, I show you Eroom's law (Moore backwards) which suggests that the cost of developing a new drug doubles every nine years.EROOM's Law [1]This is pretty unsustainable and Pharma knows it so there are a lot of experiments and proposals to change the drug development process so that failures occur earlier and successes are identified early and pushed through.Everything I will describe tackles the underlying goal of reducing the attrition rate and costs of the drug development pipeline. To briefly outline MY OWN opinions on what needs to happen to have a cost effective system I'm going to dive into:Adjusting the goals of academic research to focus on clinically relevant areas.Changing how medical research appropriately informs and guides that basic researchBridging the gaps between academic and industrial research by both academic efforts and increased drug companies' fundingDiversifying risk between the various stages of drug development by focusing on individual strengths.Revamping the clinical trial process, drug approval system, and influence of marketing to allow for smaller but faster trials.Integrating the drug distribution system into the healthcare networkCreating and rewiring the feedback loops between all of these systemsThe Role of Basic ScienceLet's start from the beginning. Academics are certainly the seed and start of medical discovery and innovation. They ask the questions we don't know the answers to and also find them and from their understanding of biology, chemistry, and medicine, the drug companies can take things to the next level.Unfortunately, the targets that are hot topics in academic will very often be undruggable targets or at least very hard to hit. Targets like protein-protein interactions, transcription factors, protein aggregates, ubiquitin modifiers, RNA, and epigenetic regulators.[2] A great example of a protein target that receives a lot of attention but is completely undruggable is p53 as described in Can p53 be synthesized into a drug to target cancer? It's a target that is extremely dynamic, has a multitude of interactions, and has plenty of off-target effects. People may get the impression that maybe one day we'll figure a way how to make it a useful drug but in reality, we'll probably never hit it and it's merely an very interesting topic of study in molecular biology (with great scientific importance I would add).The result is ~2% of the human genes are actually druggable which largely separates what academics work on and say we can cure vs. what drug companies can actually cure. [3] I describe this more in my answer to Human Genome Project: Was all the promise publicized by the media during the mapping of the human genome simply hype? and Why has genomics been so unsuccessful in the discovery of new medicines?Every now and then, one of those "undruggable" targets become druggable with the invention of new technologies or chemistries. Things like recombinant technology, antibodies, stapled peptides, and PEGylation have made it possible to attack new targets. Indeed one of the widely assumed "undruggable" targets, K-RAS, was recently targeted by a team from Max Planck using a combination of structure-based drug design.[4] Yet there is still is separation from what academics are working on and what companies can actually do.This was well stated by Stuart Schreiber (who, I note, gave me much of the structure of this portion of the argument): Academic research ... might have a greater impact if it were redirected to developing methods that change our view of what is doable. [5] While there is much talk about target-based drug discovery, the modern era hasn't produced much in terms of drugs and a large part is the failure for basic science researchers to choose good targets. [6]The fixture to this issue would be toHave real MSTPs. A good number of MD/PhDs don't end up going into research or they lose too much momentum because of residency. Having true hybrid scientists will help bridge that gap between what patients need and what is actually possible.Clearer discussions on what is and isn't "druggable". As others have mentioned, scientists should be doing a better job looking at the final product rather than thinking about what applications their recent discovery can be applied to.Improvement on target identification. People need to recognize signal from noise and unfortunately there is a lot of noise.Commitments to developing new chemistries and technologies to target the "undruggable" space.Better academia/pharma interactions.The first two are cultural things that academics need to be less stubborn about. The third is an area ripe for progress. As mentioned by Taffy Williams and Mike Thompson, the advent of personalized medicine drastically improves the ability to relate a disease to it's molecular mechanism of action. Furthermore, HTS technologies are designed to be more amenable to diseased-based drug discovery rather than target or gene-based. To better connect academic research with disease, we need to go further into the chain to medical research.Connecting Physicians and ScientistsDrug discovery really starts with observations in the clinic. Doctors will observe patients and from recognizing patterns, they will have a better idea of what makes up a disease and maybe begin to have an idea of the underlying mechanism. I go into this more in my answer to How do pharmaceutical companies go about finding cures for diseases?The problem with this model is that doctors are notoriously bad at doing science and statistics and either see patterns out of nothing or will run trivial investigator initiated trials that are under-powered, biased, non-randomized, non-placebo controlled, and poorly designed. Again, a large reason why better Medical Scientists are required in medicine.The ideal situation is better data collection using Electronic Medical Records and releasing that data from the EMR companies so that information about patient habits, diagnoses of disease can be used. Unfortunately, this information is very difficult to get unless we have a complete overhaul of the healthcare system which I will go into later. I illustrate the value of having this data with the alternative route.For now, I suggest reading the 5 part series: How to build a good EMR by Jae Won JohThe existing model for data collection is the use of patient communities like Susan Komen and Cystic Fibrosis Foundation, which have been extremely helpful to both doctors and pharmaceutical companies. It helps to link symptoms to the underlying case of disease and pools together the patient populations to understand the epidemiology and guide companies to which drugs will have the broadest effect.As seen in questions below, there are very good reasons for pharmaceutical companies to create a strong patient community to better understand the disease and to help with clinical trial enrollment.How should Pharma work with online patient communities?Which pharma companies are currently working with online patient communities?Should pharmaceutical companies support existing online patient communities or create their own?This has been extremely effective in the Rare disease community in collecting and sharing data to better inform patients, doctors, and drug companies. However, a shared worldwide network that better captures all of the variance of the disease will vastly improve physicians' ability to systematically track trends along with maintain a consistent standard of care. Furthermore, in post-approval studies, this type of network allows us to better identify side effects and drug-tolerant patient populations.The Bridge to PharmaceuticaObviously this isn't merely academia's fault. Pharmaceutical companies need to carry their weight in the drug discovery side. Given the large amount of money that is already dumped into research it is important to prioritize research funding. However, the use of those funds are currently poorly utilized.I go more into the economics in the marketing section but for now, it is important to realize that there are large non-trivial cost barriers from translating an idea from academia to company. As every startup knows, there is something called the "valley of DEATH".[7]Ignoring the y-axis "cumulative profit/loss" and replacing it with "expected value", the graph is essentially the same in the eyes of Venture Capital and investors. During the early stages of drug development, the probability of success is extremely low and expected value of the drug is equally low. Only after a lot of time and money does the "commercialization" or the proof of concept occurs and a drug becomes worth investing in.Unfortunately for the biotech industry, the valley of death usually coincides with a Phase II clinical trial which takes ~$20-100 million dollars to get to which can be demonstrated with the next figure [8].So either VCs need to start doing a series A earlier during the process and regularly fund companies pre-IND, pre-Phase III or another large player needs to step in. In addition, academic groups need to do a better job connecting their publications to the final product to reduce uncertainty and risk.This is probably the most exciting current area of drug development as it requires the least amount of momentum to achieve large meaningful results. Universities, Drug companies, and VCs are largely experimenting with how they are tacking this cap.Academics making their drug fundableI'll start with what Academics. I mentioned earlier that Academics tend to work on problems that don't usually yield to tangible results. However a deeper issue is not realizing the disconnect between a successful publication and the commercialization of that idea.The inability to draw in a licensing deal or VC funding can be summarized by:A poor understanding of the economics of the diseaseLack of meaningful clinically relevant dataAn inability in academia to weed out false positives.A poor understanding of the economics of the diseaseSince most PhDs aren't MBAs, they really have no clue how health insurance works and how much drugs actually cost. Typically the way how research is funded is:Find something coolFind what that cool thing doesSee if that thing it do is usefulJustify doing more research on that cool thing based on what it doesTotally reasonable way of doing research but it's also the reason why the NSF is getting in trouble with Lamar Smith. Essentially most of biological research is driven by finding random applications of the science rather than finding the appropriate application and making involved hypotheses to guide that science.For academics to seriously make an impact, they must first check in with the physicians to see what actually happens in the disease that they are interested in and then adjusting the drug in a manner than is suitable for that disease.For instance, several "cures" of HIV including bone marrow transplants and aggressive antibody treatments are impractical since a handful of "inexpensive" oral drugs will essentially do the same + be safer.Lack of meaningful Clinically relevant dataEveryone has seen the article "X cures cancer". What most people forget to do is to read the small text "this might be useful as a drug in 15-20 years". Typically these high-impact publications go along the lines of demonstrating efficacy in an early model system and then following up those observations with the next logical series of experiments.The common saying in the drug discovery world is that "you get what you screen for". As critics of the pharmaceutical industry will say, we're good at curing mice. While we still face the same drug development issues when we attempt to treat mice, the result remains the same, our drug discovery pipeline isn't optimized for finding drugs that treat human diseases. That is, things like chemical-based screening and target-based screening doesn't necessarily produce clinically relevant results. As mentioned, later, the major sources of failure come from lack of efficacy or toxicity. This basically suggests that you've chosen the wrong target to attack.The alternative is to design the screens to identify clinically relevant compounds from the start. Using disease-specific cell-based assays are one method. Using several filters for activity is another. There are also several efforts to build better mice models which actually have human immune systems and die from human cancers. The world of iPSCs also opens the door to the creation of immortalized cells that come directed from a diseased patient.The ideal scenario is to change drug discovery from a linear process to an integrated research pipeline which eliminates false positives from the start. I'll go more into the research integration later.Proposal for bridging the valley of death [9]An inability in academia to weed out false positives.Certainly a sensitive topic in research is the question Is most medical research wrong? Why or why not?A classic paper Why Most Published Research Findings Are False by John Ioannidis suggests that there is an unfortunate tendency for publications to select for positive data. In my own answer, I claim that this falsehood comes from the misinterpretation of the data and answer by Manish Kothari and Michael W. Long also go along those lines. It surprisingly isn't because of fraud or data manipulation, it's more that people are pressured into seeing what they want to see and making the wrong analysis.This issue also reflects the very difficult task of reproducing research. As a personal example, we have one company that is trying to replicate our data using a similar experimental setup and they were failing to do so. In fact, they had to send "experts" to directly observe my labmate doing his experiment and even made him use their own reagents to confirm. Ultimately we narrowed it down to them using a poor source of a few reagents along with forgetting to mix certain chemicals in a certain order. Unfortunately the guy who figured this all out left so after we taught this information, we had to teach it all over again.A lesser known study done by Bayer and Amgen formed validation teams that essentially spend a year trying to reproduce other people's data. Their conclusion: ~20-25% of the data was reproducible; 2/3 of the data there were inconsistencies. [10]Again, this isn't because of fraud or data manipulation. In many of these cases, the teams had to replace cell lines or change the assay formats to get the hypothesis to work. However, even then, there were inconsistencies. There is a lot of variation in biology and are several factors that may cause a false positive.The moral is: Just because your paper was accepted in Nature, it still doesn't mean that it's scientifically sound enough to spend $1 billion dollars on it. To successfully and scientifically validate your idea to the point where a company is willing to take a risk requires several confirmations of your idea. If the drug works in an assay, use a new assay; if it works in another assay, use a cell-based assay; if it works in a cell-based assay, use another cell-based assay; if it works in that cell-based assay, use a mouse; if it works in a mouse, use a rat; etc. See How do drug researchers address effects that only occur in rats?For intellectual pursuits, these studies may not be particularly rewarding but they are the scientifically correct thing to do and ultimately brings in investors. There is also the whole revamping of the publishing model which I will also go into later.I conclude this chapter with a brief telling of The Sirtuin Saga regarding Resveratrol.[11]Triggered by a study by David Sinclair in 2003 that suggested that the molecule in red wine extended the lifespan of yeast cells and at one point showed the reduction of aging in mice. The resulted in the starting of the biotech company Sirtis which ultimately got acquired by GSK for $720 million. However, later studies suggested that the in vitro assays that suggested this activity had some artifacts due to the presence of the fluorescent molecule used in the experiment. Recent data suggests that the assay only worked in specific conditions but still worked. In the end, the scientists did isolated activity, they just started a ~$1 billion company off the wrong lead compound. [12] [13]This debate itself has lead to multiple Quora questionsDo sirtuins really lengthen lifespan?Does resveratrol keep our cells from aging?Why is red wine good for you?If you ask Alex K. Chen for his opinion, the answer is maybe.Academic pipelinesIn order to get researchers to recognize these pitfalls, Universities have created internal pipelines to help academically minded people solidify business-friendly science that can be outsourced.Most of these groups help Professors and students get through the hurdles mentioned above and uses industrial expertise to indicate what risks remain with the proposed technology. Ultimately these ideas would become mature enough to be licensed or spun out into a company and allow Professors to get back to their professing.A few of these programs already exist and the best examples includeStanford SPARKMIT NEWDIGSNorthwetern CMIDDEmory Institute for Drug DevelopmentU Toronto MaRSUCSF CDDSDrug companies funding academicsAs the academics reduce their risk, companies and VCs need to do a better job taking risks. There are usually two schools of though on how to approach this problem.Drug companies should start pulling out the checkbooks and with an aggressive M&A or partnerships fund early stage researchDiversify the risk to Contract Research Organizations and let them handle early stage Clinical development.Those who believe in virtual and lean startups will tell you to go with option 2 and since they have a MBA, they are probably right. I will tell you to go with option 1 since only Pharmaceutical companies had the long term discipline and vision to prevent further fragmentation of an already shaky potential drug. The current reality is something in the middle since Pharma companies are too unweldy to move quickly through development and small biotechs are too desperate to do good science.The ideal model is a Pharma funded early stage pipeline program that operates independently of the mother program but has the financial and intellectual capital to success.Good examples of these early pipeline programs includeGenentech (gRED) / RocheChorus / LillyCORTEX / PfizerCentocor/ JnJBad examples of early pipeline programs that weren't independent includeGroton / PfizerSandwich / PfizerKalamazoo / PfizerWyeth / PfizerKent / PfizerSirtris / GSKResearch Triangle / GSKHarlow / GSKWhitehouse Station / MerckBasically don't be Pfizer. What essentially happened was that pharmaceutical management interfered with early R&D and started outsourcing certain functions to other countries with "expertise" for the sake of "efficiency". However, what that actually means is waiting for a chemist in China to ship their compound to the assay development team in North Carolina which uses a protein created in Switzerland. It's pretty much guaranteed to not work. What you really want is a small well funded mini biotech that cranks out a bunch of compounds.GSK had shifted to a Therapy Area Units (TAUs) system but they are in trouble since they keep on changing the model every 8 years whenever they get a new CEO. Novartis uses the NBIR model; JnJ never bothered to integrate their units; Roches has pRED and gRED; Merck stuck with MRL but the new R&D chief is proposing an aggressive reshuffling at the time of writing. [14]Pfizer has changed their research model from independent research labs to "Centers of Therapeutic Innovation" which collaborate heavily with several Universities. They are essentially outsourcing all of their R&D to academic labs. Probably a wise move but probably not worth imploding their research units.In summary: Pharmaceutical Managers needs to stop moving units around every ten years especially involving products that take 15 years to work.Part of this is a disciplined approach to outsourcing which gets back to the MBA's approach to doing research. There is a lot of value in contracting out research.It allows companies to focus on what they are good at.Reduces training time of new hiresSpreads out capital costs (especially with contract manufacturing)Where this quickly goes wrong is when expertise gets lost and communications gets severed. I mentioned my own outsourcing story earlier and Derek Lowe has several deep and bitter discussions on the problems of outsourcing. When outsourcing causes you to spend more time troubleshooting your supply chain rather than doing science, you're sacrificing time and money. [15]Making Marketing Departments SHUT UPAccording to Adithya Balasubramanian's answer to What is the detailed cost breakdown of an expensive clinical trial? ~90% of the cost to approve a single drug comes during the Phase III clinical trial. Phase III trials are expensive and unfortunately still fail 40% of the time.The major reasons behind failure: efficacy and toxicity. [16]At some point we had information from Phase II trials that informed us that this drug had a pretty good shot at working. As indicated in the cost breakdown, In addition to the fact that they already cost a lot, Phase III trials are get more expensive because they are getting longer, more complicated, have a lower patient retention rate, lower patient enrollment rate. All in all, we are being too aggressive with the way how we design clinical trials and pushing compounds into phase III.A good example of getting impatient and going blindly into Phase 3 trial was the recent Pfizer, JnJ, and Elan efforts with Alzheimer's and blew over $1 billion on two trials with Bapineuzumab despite very indifferent Phase II data. See Where did most of the money in the failed Bapineuzmab Alzheimer’s antibody phase III trial go? The companies had their eye on the $5 billion / year Alzheimer's market but didn't allow the science to dictate their strategy and placed a bad gamble. [17]My hypothesis is that we rush to Phase III too quickly and design the trials to be too broad. If the Phase II data indicates that the drug works in half of the patients, we should be testing the drug in the responsive half. The marketing team will say, that's too complicated, let's test all of the patients and get twice as much money obtaining a blockbuster.The unfortunate result is to appropriately design a suitable trial, you will need a larger subject population and a longer enrollment period to sufficiently power the trial. This ultimately will cost several times more and has a higher chance of failure than designing a smaller well powered trial. This comes back to efficacy and toxicity. If you have a good idea which patients will likely show the best efficacy and least toxicity, you should design your trial for those patients. As mentioned earlier, this is partially due to choosing an incorrect target. However, enrolling the patients that have the wrong target is also a sure way to get a dud.The clinical trial that goes against this tide was Herceptin which looked to attack a gene that was expressed in only 20% of Breast Cancers. From a disciplined scientific approach, Genentech resisted the pressure of increasing their market size 5x by narrowing into the smaller group of Her2-positive patients. Doing so allowed them to use a significantly smaller population (10-20 times smaller) and get approval more quickly.However, there is a reasonable question whether the marketing people were right. Recent reports do suggest that Herceptin works even for certain Her2 negative patients and a non-trivial proportion of Doctors don't pay attention to Her2 status prior to prescribing the drug. In the end, you want the broadest indication since it gets you the most money in the brief period of time the drug patent exists. Marketing got their Blockbuster anyways.You can't blame Pharma companies for thinking this way. I;m sure that some MBA has shown that taking these type of these aggressive gambles should actually get more money in the short term despite the higher failure rate. As a result, to incentivize a trend towards these smaller but better designed trials we need to have an overhaul of the drug approval process.A new interaction between the FDA, Insurance, and PharmacyWhile a lot of drugs fail simply because we didn't identify failiures early like in the case of Bapi, there are some drugs that failed since too many non-responders were enrolled. In my non-medical opinion, drugs like Vioxx and Avandia should probably be on the market since they do work despite what Steve Nissen says. Their problem is that they are being marketed to the wrong people.Despite the large controversy in data reporting for Rofecoxib (Vioxx), it was an extremely effective drug for some patients. It certainly had risks but both the US and Canada voted in favor of allowing the drug to be returned since they thought that the benefits outweighed the risks. However, the publicity hit already happened and Merck decided to permanently withdraw the drug.Another good drug that had devastating side effects was Thalidomide, the drug that triggered the strengthening of the FDA in the first place. The drug infamously caused numerous birth-defects and was quickly withdrawn from the market. As we can see in Can Thalidomide -- a drug with an exceptionally controversial history -- actually be used to treat multiple diseases as claimed in the article cited below? the once dangerous drug has reemerged as a potential Multiple Myeloma drug.This brings us into need to change the drug approval process. The potential of Pharmacogenomics can significantly change our ability to rationally identify patients who will respond well to drugs. This allows us to better design clinical trials that will enroll patients.The risk of these trials are still high and there are real concerns about generating enough of a profit to bridge the valley of death.The appropriate proposal is the use of adaptive licensing, which takes advantage of accelerated approvals to start charging patients to recuperate the costs of drug development but under extremely restrictive conditions. However, while it may cost more per patient initially, it does lower the barrier to entry and reduces the time spent in the valley of death.Depiction of Adaptive licensing [18]A good example is the recent approval of Lomitapide which ran a tiny 29 patient phase III trial for the ultra-rare genetic disease homozygous familial hypercholesterolaemia and got FDA and EMA approval for only that indication. However, the compound has potential efficacy in hetereozygous patients and with the initial approval in the small patient population, they should have enough cashflow to initiate the larger phase III trials.This is heavily on the FDA to allow these trial designs to occur. Their role is to ensure that efficacy and safety are in place. With that in mind, they need to reassure companies that they won't consider these early stage Phase III trials as "proof of concept" trials and are willing to look at earlier NDA fillings.It should be acknowledged that reducing the patient population does complicate enrollment and as indicated by several answerers in What are some of the biggest challenges with setting up and conducting clinical trials? enrollment is one of the hardest steps with clinical trials. However, by tapping in to the patient communities and the use of smaller trial designs, I am hopeful that this dilemma can be resolved.Closing the Feedback LoopTypically when you see something about drug development you see funnel like this:I've always hated this diagram.It makes the entire drug development process seems extremely linear and essentially the secret to getting a drug is taking more shots. Also it assumes that failure is built into the process.The real drug development diagram looks more like the next two diagrams [19]The key thing that makes these proposed systems work is the ability to use the current data to better design future experiments. Rather than working on several compounds and removing them by a process of elimination, you're working on a single product that gradually gets refined and polished by the time it reaches approval. Failures should lead to new hypotheses and guide the development rather than close the door. For this loop to be complete several things need to change.Doctors and InsuranceThese folks were blamed before but now they are getting blamed again. For adaptive approvals to work, Doctors will have to restrain one of their most powerful tools: off-label use. At the same time, insurance companies need to do a better job enabling off-label use when it is appropriate.As mentioned, Doctors need to do a better job observing and reporting patient outcomes. With the increased role of Phase IV monitoring this becomes even more important. Doctors will also need to adjust to the increased role of companion diagnostics and personalized genomics information. For instance, an abnormally large percentage of doctors prescribe herceptin without checking their patient's HER2 status. While the next wave of doctors are beginning to be trained with this mindset, a full overhaul of medical practice won't occur for at least another 30 years when veterans finally die out (however, we still want our Drs on Quora to live forever). However, even the current medical education that was given to people like Jae Won Joh and James Pan doesn't fully integrate a mindset of using personalized medicine.Insurance companies will also need to shift from a high-deductible mindset to a preventative mindset. Drugs in the US are still extremely expensive to the end-user and insurance companies aren't doing enough to negotiate those prices down and appropriately. They will also need to shift from a disease-based model to a target-based model. We can no longer treat breast cancer as breast cancer but instead, treat HER2-positives vs. EGFR-positive cancers. With these systems in place, drug repurposing would become more easy to recognize and push through.Completing this side of the feedback loop will be a key step. For this to happen, Electronic Medical Records will have to be commonplace and better and systematic data collection needs to be implemented.The interesting arena of clinical trials with personalized medicine are the MD Anderson BATTLE Trial and the British Columbia Cancer Agency's Utilization of Genomic Information to Augment Chemotherapy Decision-making for People With Incurable Malignancies in combination with PREDICT. These efforts use full genome sequencing from single-patients in attempts to do personalized cancer treatments. However, according to Marco Marra (I saw him at a conference), there are all sorts of logistical hurdles including biopsy collection and access to off-label drugs. There is also the whole inability to making meaningful connections between genomic datasets and the root cause of cancer.Revamping Data TransparencyAs it can be seen in questions likeMedical Research: Are a significant fraction of drug studies private and not released out to the public?Food & Drug Administration: What is the best way to track the progress of experimental treatments undergoing clinical trials?Where can I find meta-analysis reports on clinical trials?Is every clinical trial recorded on ClinicalTrials.gov?Is that true the clinical trials or biology research data in university labs are chaos and not recorded properly for a feasible usage of the co-worker and the succeeded researcher?Is most medical research wrong? Why or why not?There are all sorts of problems with collecting and releasing data. Again, it's not as if we're doing all sorts of fraud and making stuff up (at least most of us). I've already talked about this extensively in the section: An inability in academia to weed out false positives. In addition to the steps to validate those results, another major change would be to incentivize the publication of more negative data.To ask private companies to publish negative results, we must first ask this of our own government sponsored researchers. The bias towards positive data and the lack of acknowledging the negative data is a huge problem in academia and often leaves well intentioned hypotheses to linger longer than they should. The reasons are numerous as described in Why don't academics regularly publish their negative results?However, there are new better outlets for publishing these results and with the onset of new low-cost open-access journals like PeerJ and PLoS One, the barriers toward publication are being reduced. Ultimately it will take a massive culture change before that happens.GSK and others have also recognized that lack of data sharing in Clinical trials have also hindered their ability to predict potential failures. Unfortunately, the current situation is a prisoner's dilemma where companies that share the data get hurt by the companies that don't but all of the companies would greatly benefit if everyone shares. However, through outside political pressure from the NIH and FDA along with internal pressure, this should be a dilemma that gets resolved as the inertia changes. [20] You can find out more at All Trials Registered. There is also an extensive discussion by Ben Goldacre that is summarized in What do medics, researchers, drug company employees or drug regulators think about 'Bad Pharma'?There currently is a lot of valuable data out there. Genome sequencing has open the floodgates in genotype information and doctors see interesting observations all the time. However, there isn't an efficient system capturing all of this knowledge and despite all of the hate towards the Patient Protection and Affordable Care Act, if it accomplishes anything, it will be the mandated migration to EMRs.The SummaryCongrats. You are approaching the end of this giant tirade about the stiff and stubborn drug making complex. I hope that people understand that there are a lot of factors at play and the high cost of drugs isn't entirely the drug industry's fault. In addition, there are complicated politics that prevent the major players from interacting. Do to this, we need to do a better job passing a compound from one stage to the next.Academics need to do a better job convincing companies of their science.Doctors need to do a better job relying their problems to the academics.Pharma companies need to do a better job funding the researchers.Pharma needs to do a better job designing their trials.The FDA needs to do a better job allow people to design these trials in that manner.Everyone needs to publish their data.At the end of the day, it comes down to having a system where science is relevant to the medicine and guides the drug development process. The industry needs to shift to a system where hitting singles and getting compounds through is more cost effective and efficient than swinging for homeruns. This is a topic that I talk about quite a bit and I suggest following In the Pipeline: and the Quora blog Making Drugs.[1] Diagnosing the decline in pharmaceutical... [Nat Rev Drug Discov. 2012][2] Outsmarting Cancer: Why It's So Tough[3] Druggability[4] Small molecule inhibition of the KRAS-PDEδ interactio... [Nature. 2013][5] The State of the Art of Chemical Biology[6] A critique of the molecular target-based drug disc... [Metab Eng. 2008][7] Osawa and Miyazaki, 2006[8] Organic synthesis toward small-molecule probes and drugs[9] http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0020124[10] Believe it or not: how much can we rely ... [Nat Rev Drug Discov. 2011][11] The Sirtuin Saga[12] Thinning the Fog around Sirtuins | Guest Blog, Scientific American Blog Network[13] http://www.sciencemag.org/content/334/6060/1194.full[14] Making Changes Inside Merck's R&D[15] An Outsourcing Blast[16] Translational research: 4 ways to fix the clinical trial[17] How A Failed Alzheimer's Drug Illustrates The Drug Industry's Gambling Problem[18 ] Adaptive Licensing: Taking the Next Step in the Evolution of Drug Approval[19] Discovery of small molecule cancer drugs: successe... [Mol Oncol. 2012][20] GSK commits to publishing all clinical trial results (Wired UK)