Archive for November 2016
By: Sterling Payne, B.Sc.
Minimally invasive surgery has been around since the late 20th century, however, technological advancement has sent robotic surgeons to the forefront of medicine in the past 20 years. The term “minimally invasive” refers to the performance of a surgery through small, precise incisions a far distance away from the target, thus having less of a physical impact on the patient in terms of pain and recovery times. As one can imagine, surgeons must use small instruments during a minimally invasive procedure and operate with a high-level of control in order to perform a successful operation. In light of these requirements, and due to fast-paced advances in robotics in the last decade, robots have become more common in the operating room. Though their use benefits all parties involved if used correctly, several questions of policy accompany the robotic advance and the goal of fully autonomous surgery.
The da Vinci system is one of the most popular devices used for minimally invasive surgeries, and was approved by the FDA in 2000 for use in surgical procedures. The newest model, the da Vinci Xi® System, includes four separate robotic arms that operate a camera and multiple arrays of tools. The camera projects a 3D view of the environment onto a monitor for the surgeon, who in turn operates the other 3 arms to perform highly precise movements. The da Vinci arms and instruments allow the surgeon more control over the subject via additional degrees of freedom (less restricted movement), and features such as tremor reduction.
Though the da Vinci system is widely used, its success still depends on the skill and experience of the operator. Surgical robotics engineer Azad Shademan and colleagues acknowledged this in a recent publication in Science, highlighting their successful design, manufacturing, and use of the Smart Tissue Autonomous Robot (STAR). The STAR contains a complex imaging system for tracking the dynamic movement of soft tissue, as well as a custom algorithm that allows the robot to perform a fully autonomous suturing procedure. Azad and colleagues demonstrated the effectiveness of their robot by having it perform various stitching procedures on non-living pig tissue in an open surgical setting. Not only did the STAR succeed in both procedures, it outperformed highly experienced surgeons that it was pitted against. More information on the STAR can be found here.
In response to the da Vinci system, Google recently announced Verb Surgical, a joint-venture company with Johnson & Johnson. Verb aims to create “a new future, a future unimagined even a few years ago, which will involve machine learning, robotic surgery, instrumentation, advanced visualization, and data analytics”. Whereas the da Vinci system helps the surgeon perform small, precise, movements, Verb will use artificial intelligence amongst other technologies to augment the surgeon’s view, providing information such as anatomy and various boundaries of bodies such as tumors. A procedure assisted by the da Vinci system can increase the physical dexterity and mobility of the surgeon, however, Verb aims to achieve that and give a “good” surgeon the knowledge and thinking modalities previously confined to expert surgeons gathered over time through hundreds of surgeries. In a way, Verb could level the playing field in more ways than one, allowing all surgeons access to a vast knowledge base accumulated through machine learning.
As proven by the introduction of fully self-driving cars by Tesla in October, autonomous robots are becoming integrated into society; surgery is no exception. A 2014 paper in the American Medical Association Journal of Ethics states that we can apply Isaac Asimov’s (author of I, Robot) three laws of robotics to robot-assisted surgery “if we acknowledge that the autonomy resides in the surgeon”. However, the policy discussion for fully autonomous robot surgeons is still emergent. In the case of malpractice, the doctor performing the operation is usually the responsible party. When you replace the doctor with an algorithm, where does the accountability lie? When a robot surgeon makes a mistake, one could argue that the human surgeon failed to step in when necessary or supervise the surgery adequately. One could also argue logically that the manufacturers should claim responsibility for a malfunction during an automated surgery. Other possibilities include the programmer(s) who designed the algorithms (like the stitching algorithm featured in the STAR), as well as the hospital housing the robot. This entry from a clinical robotics law blog highlights the aforementioned questions from a litigator’s standpoint.
A final talking-point amidst the dawn of autonomous surgical technology is the safeguarding of wireless connections to prevent “hacking” or unintended use of the machine during telesurgery. Telesurgery refers to the performance of an operation by a surgeon who is physically separated from the patient by a long distance, accomplished through wireless connections, at times open and unsecured. In 2015, a team of researchers at the University of Washington addressed the weaknesses of the procedure by hacking into a teleoperated surgical robot, the Raven II. The attacks highlighted vulnerabilities by flooding the robot with useless data, thus making intended movements less fluid, even forcing an emergency stop mechanism. Findings such as this will help with the future development and security of teleoperated surgical robots, their fully autonomous counterparts, and the policy which binds them.
When a web browser or computer application crashes, we simply hit restart, relying on autosave or some other mechanism to preserve our previous work. Unlike a computer, a human has no “refresh” button; any wrongful actions that harm the patient cannot be reversed, placing a far greater weight on all parties involved when a mistake is made. As it stands, the policy discussion for accountable, autonomous robots and algorithms is gaining much-needed momentum as said devices inch their way into society.
Have an interesting science policy link? Share it in the comments!
By: Brian Russ, PhD
Scientific funding can be a very tricky proposition. Unfortunately, there is a finite amount of money that is put towards science each funding cycle. This means that at any given time funding agencies need to decide where they believe their funds will be best spent. Every funding cycle, one can find different groups lamenting that their favorite topic is “being underfunded” while some other group is getting “too big a piece of the pie”. There is often no right answer to the question of how much is the right amount of funding to provide different topics, and the likelihood is that at the end of the day every group will feel that they are not getting the right amount of respect and funding.
This debate has come to the forefront recently in the fields of psychiatry and neuroscience with a change in the leadership at the National Institute of Mental Health (NIMH). In September, Dr. Joshua Gordon became the new director of the NIMH. Dr. Gordon’s directorship of the NIMH comes after a 13-year period of leadership by Dr. Tom Insel. During the previous administration, there had been an increasing focus on funding neuroscience related work, often at the expense of purely behavioral work, such as cognitive behavioral therapies for psychiatry patients. It is important to point out that the NIMH’s definition of neuroscience research includes basic, translational, and clinical neuroscience research. This direction led to a new research framework for studying mental health disorders termed the Research Domain Criteria (RDoC), which has a very strong neuroscience component. The goal of RDoC is to provide a new framework in which researchers and clinicians can study and treat mental health disorders. The RDoC framework involves neuroscience components of brain circuits and physiology, and cognitive components of behavior and self-reports. The end goal is to provide a more comprehensive description of mental health disorders with the intention of developing cures and treatments. This push toward RDoC, and more neuroscience in general, has led to both praise and criticism of where the NIMH is directing its funding opportunities.
Recently, an opinion piece was published in the New York Times stating that the NIMH needs to reverse their push towards more neuroscience. Specifically, Dr. Markowitz, a research psychiatrist from Columbia University, believes that the NIMH has been funding neuroscience at the expense of clinical psychological research, in the absence of a brain oriented component. His argument is that in the current funding environment only 10% of the NIMH’s research budget is going towards clinical research. From the content of his article the research he is speaking of involves behavioral studies and interventions that contain no neuroscience component. Dr. Markowitz brings up many important points, and his main thesis that we cannot forget about behavioral interventions while pursuing the biological bases of clinical disorders is critical. For example, he makes the strong point that neuroscience research is unlikely to help solve the problem of suicide. And his final argument is for a “more balanced approach to funding clinical and neuroscience research.”
However, one can argue what that balance should actually look like. Is ten percent of the budget actually a small amount? And does that number include the multitude of basic neuroscience studies that are investigating the neural underpinning of a given disease? For example, based on the NIH reporter, schizophrenia research has been funded for approximately 250 million dollars for each of the last three years. A quick look at the total budget (32.3 billion in 2016, with ~25 billion going to research grants) suggests that that would be on the order of about 1% of the total NIH research budget. This is only one disease, and is being calculated from the whole NIH budget, not just from the NIMH budget. Only a portion of that funding is going towards clinical research, as Dr. Markowitz would define it, however the rest of that funding is going to research that will in all likelihood provide clinical benefits to patients down the road, in the form of new physiological targets or potentially new drugs.
So how can one make a determination about the correct of amount of funding that should go towards different mental health fields? Should 25% or 50% of the budget go towards clinical research? It seems that comparing the percent of money going to clinical research versus neuroscience is simply a bad comparison. Neuroscience is not one homogenous topic; it includes tens of, if not over a hundred, different fields. The various mental health fields fighting each other over funding doesn’t help anyone. Both neuroscience and clinical research need to be funded. It seems that the best way to divide the funding from NIMH would not be to specify what field gets priority but instead to fund the best grants regardless of whether there is a specific component involved. This would open the door to more clinical research while not requiring a shift in the priorities of the NIMH, whose mission is to understand and treat mental illnesses though both basic and clinical research. For instance, RDoC already contains both behavioral and self-report components. These components should be given as much priority as the other neuroscience components. If 10% of the budget is given to behavioral work, in this way, that would seem reasonable, possibly even greater than other areas might be getting.
On a final note, while we should always be looking internally at how we are funding different types of science, and if we, the public, are getting our money’s worth out of projects, it is also important for us to ensure that science funding as a whole is increasing. The current funding environment has been relatively static for years. We need, through advocacy and outreach, to get the public and government to provide more funding opportunities to the NIH. As the saying goes “a rising tide raises all boats”.
Have an interesting science policy link? Share it in the comments!
By: Kseniya Golovnina, PhD
On November 8th 2016, nine states voted on legalizing recreational and medical marijuana (Cannabis L.). All US presidential candidates supported relaxing current restrictions on marijuana use. Since 2011, more than 50% of Americans consistently show positive attitudes towards legalizing marijuana. It is estimated that marijuana industry tax revenues for federal, state and local governments could total up to $28 billion. In addition to tax revenues, the non-profit advocacy group the Drug Policy Alliance highlights that marijuana legalization will reduce harm to young people and people of color, create new jobs, save money on law enforcement, and promote development of tests for drug impairment.
One of the challenges in marijuana regulation is how it is generally perceived—either as a drug or as a harmless recreation. On the one hand, it is a central component of the long standing ‘war on drugs’ that is a primary part of US law enforcement. According to the legal system, marijuana remains classified as a Schedule I substance under the Comprehensive Drug Abuse Prevention and Control Act of 1970, along with heroin. On the other, prominent thinkers argue that it is a drug of choice, without a known lethal case, which helps produce serenity and insight, and should be regulated as alcohol and tobacco. Recent policy shifts will strike a new balance between these views.
While prohibited at the Federal level, marijuana decriminalization laws have been passed in several states by lawmakers, and often through public ballot measures. In 25 states, Cannabis is legal for medical use and in 5 states, for recreational use. Out of the 9 states that voted on Nov 8, only Arizona hasn’t supported marijuana initiatives. In 2013, the Obama administration clarified Federal marijuana enforcement to deemphasize some criminal behavior, and remain in harmony with new and evolving state laws. The US Congress is acting as well, with a petition introduced (the CARERS Act) intended to remove conflicts between state and federal laws.
Marijuana in science
Shown clearly by these recent political trends, the public attitude has been shifting rapidly, and legalization appears to be only an issue of time. From a scientific point of view, legalization of Cannabis will open the door for robust federally approved research on marijuana’s therapeutic value. The reasonable scientific question now is whether and to what degree Cannabis can be a real new frontier of therapeutics?
Marijuana chemical science started from the identification of THC (delta-9-tetrahydrocannabinol) as the main active ingredient. Today, more than 460 chemicals are known to be Cannabis ingredients, more than 60 of which are grouped under the name cannabinoids. In the early 1990s, cannabinoid (CB) receptors were discovered and cloned. Cannabinoids, along with their receptors, make up the endocannabinoid (EC) system, which participates in the regulation of neurotransmission. Surprisingly, a number of chocolate-derived chemicals can activate the human cannabinoid system, both directly and indirectly, suggesting that chocolate and marijuana can have overlapping effects. The identification of natural agonists anandamide and 2-arachidonylglycerol, which also act on CB receptors, has stimulated interest in the medical uses of Cannabis. On PubMed the number of publications with the term “cannabis” has increased from 71 in 1990 to 1195 in 2016, revealing both the unexpected Cannabis therapeutic horizons and warnings about its effect on adolescent brain.
A 2003 review on cannabinoids as potential anticancer agents reported, “cannabinoids have favorable drug-safety profiles and do not produce the generalized toxic effects of conventional chemotherapies.” Thirteen years later in 2016, cancer therapy using cannabinoids is still paradoxical but evident. In 2006, based on the analysis of 72 controlled studies evaluating the therapeutic effects of cannabinoids, it was shown that “cannabinoids present an interesting therapeutic potential as antiemetics, appetite stimulants in debilitating diseases (cancer and AIDS), analgesics, and in the treatment of multiple sclerosis, spinal cord injuries, Tourette’s syndrome, epilepsy and glaucoma”. A potential antipsychotic effect of cannabidiol was also reported in 2012. At the 2015 AAAS Annual Meeting, researcher Mark Ware from McGill University Health Centre in Montreal, Canada, reported, “it’s clear that the weight of evidence now is such that cannabinoids are analgesic drugs,” while also emphasized that more studies are needed to understand the best dosing and delivery methods for medical use.
A search on the website ClinicalTrials.gov, maintained by the National Institutes of Health shows 557 clinical trials with ‘known status’ for the term “cannabis” as of October 26, 2016. More than one hundred of them are open now. Topics for these studies relate to Cannabis abuse as well as new treatments for a variety of medical conditions such as schizophrenia, cancer, autoimmune diseases, epilepsy, musculoskeletal diseases, and others. For example, GW Pharmaceuticals Ltd. was conducting clinical trials with Nabiximols (trade name Sativex) to investigate its safety in treating cancer pain. However, out of ten cannabis-related drugs on the world market, only three (including Sativex) are approved for medical use in the US.
Legalization, public interest and scientific research on Cannabis has promoted regulatory agencies such as the Food and Drug Administration (FDA) to develop new policies and guidance. It is stated on the official FDA website that “the FDA supports researchers who conduct adequate and well-controlled clinical trials which may lead to the development of safe and effective marijuana products to treat medical conditions.” Non-profit US Pharmacopeial Convention (USP), a known leader in developing and controlling drug standards, has organized a Cannabis expert committee to develop USP Standards for medical Cannabis. Their aim is to control quality specifications for the Cannabis used in clinical studies.
While the frontier of science appears to be opening for Cannabis in the US, the regulatory regime will need to keep pace. As medical use legalization proliferates, there will be a strong, even urgent need to revamp regulation to accommodate and emphasize research and best uses. Until the regulations are properly developed there will be some uncomfortable unknowns from a public health perspective, leading to greater risks and missed benefits.
Have an interesting science policy link? Share it in the comments!