Keynote speech on the JAVELIN not going far enough to improve survival
The treatment landscape for metastatic renal-cell carcinoma has changed dramatically with the introduction of immunotherapies. Unfortunately though, we are promoting combinations over single agents without having much idea of added benefit of each drug. This is an important issue because when we combine two drugs, the only thing we are certain of are the added toxicities. PD-1 inhibitor nivolumab had improved OS when given in second line, however nivolumab was tested in combination with ipilimumab (not as a nivolumab monotherapy) in the first line trial. Now, pembrolizumab and avelumab have followed suit, although their combination partner was axitinib – a VEGF inhibitor. The control arm was sunitinib for both of the trials of pembrolizumab plus axitinib (KEYNOTE 426) and avelumab plus axitinib (Javelin 101). This is a little surprising because we are testing A B versus C, where both A and B haven’t been approved for the given setting – axitinib was approved for RCC in second line. Both these combinations improved PFS versus sunitinib but only the pembrolizumab combination has shown improved OS. However, I have doubts about the contribution of axitinib to these results. What would the outcome be if pembrolizumab alone is followed by sunitinib in second line? It is important to note that only one third of patients who discontinued sunitinib received PD-1 inhibitor subsequently in the KEYNOTE 426 trial. The important question for patients and clinicians would be to consider a survival difference had most of these patients received a PD-1 inhibitor subsequently. As for avelumab, the JAVELIN trial hasn’t reached as far as pembrolizumab and nivolumab have reached: The OS benchmark – so let’s reserve this combination until we see that benefit.
Have we successfully landed on the COMET?
We should remember that this combo-mania with PD-1/PD-L1 inhibitors may also backfire. Previously, the RCTs of nivolumab and pembrolizumab combos were halted in multiple myeloma for higher deaths in the combo arms. Another RCT IMblaze 370 also reports that atezolizumab, alone or in combination with cobimetinib, failed to improve survival versus regorafenib in patients with metastatic colorectal cancer. This time again A B failed versus C although C in itself is a drug with very marginal benefits in this setting. Also, I don’t understand testing A plus B combo when both A and B are unapproved for the disease.
Well, you might not have noticed that my blogs were missing for the last three months but anyways, its good to be back. I was having a little time off blogs and social media as I was transitioning in my career but now I am back. Sometimes, it is very difficult to manage time for things that you must do versus things you enjoy doing, especially when these two don’t intersect. For me, these last few months the things I had to do were all bureaucratic while I couldn’t find the time for things I enjoy doing like writing these blogs. But now that we are back, let’s recap what has happened in the oncology world in the year 2019 so far. I can’t cover all of them, but will try to summarise the major events in oncology.
Hundred Foxes’ Howl versus One LION’s Roar
In my country, there is a saying that goes somewhat like the roar of one lion will scare hundreds of howling foxes away. In medicine, I guess, it translates as one good RCT trumps the results from hundreds of observational studies. For patients with advanced ovarian cancer, primary surgery to achieve complete resection is the most important treatment and prognostic factor. However, what to do with the lymph nodes is a question that has troubled the oncology community for a long time. Logically, it makes sense to remove the lymph nodes too because they are the sanctuary sites for cancer cells. However, lymph node dissection carries high morbidity. Although multiple observational studies suggested a survival benefit with lymph node dissection, the LION trial, now published in the NEJM, shows that for women with macroscopic complete resection of primary tumour, lymph node dissection increases morbidity (postoperative complications) and post-operative mortality rates but doesn’t improve survival. I am glad that this trial was carried out and these results will now save many women with ovarian cancer worldwide from unnecessary harmful procedures, but I am also sad that we didn’t answer this question until now and thus, many patients suffered unnecessarily. I hope this LION’s roar scares us from jumping to conclusions based on logic or observational data alone and without RCT evidence in future. Another lesson here is the importance of public funds in supporting RCTs like these.
September was an important month in oncology—especially for lung cancer. The World Conference in Lung Cancer (WCLC) 2018 gave us some important practice-changing results, also leading to four NEJM publications. The trial with most public health impact is unfortunately not published yet. It’s the NELSON trial that randomised more than 15000 asymptomatic people at high risk of lung cancer to either CT-based screening for lung cancer or to no screening and found a significant reduction in lung cancer mortality rates among the screened cohort compared with the control cohort. This reduction was more pronounced among women, although they constituted only 16% of the trial population. I am looking forward to reading the full publication and am particularly interested in knowing if there were any differences in all-cause mortality rates and the rates of overdiagnoses.
A new ALK-inhibitor on the block—brigatinib—has significantly improved PFS versus crizotinib when used as first-line therapy in ALK-positive non-small cell lung cancer (NSCLC) patients. However, I assume that it will be difficult for brigatinib to replace alectinib in this setting, since the latter has already been tested in two different RCTs and has more mature data.
With Keynote 407, pembrolizumab has entered into the treatment arsenal for squamous NSCLC by improving overall survival in combination with chemotherapy versus chemotherapy alone as a first-line regimen. However, when A B is compared with A, it is important to know whether A B is better than A followed by B. In this trial, 32% of patients who were in the control arm received a PD-1 inhibitor upon progression. Nivolumab is already approved as a second-line option in this setting after first-line chemo; so how much benefit in Keynote 407 is due to more than half of control arm patients not getting PD-1 inhibitor at all versus the benefit of combining pembrolizumab with chemo upfront is an important question.
There was a very sobering piece in NEJM by the FDA last month in which the authors try to explore what went wrong with the Keynote-183, Keynote-185 and checkmate 602 trials testing PD-1 inhibitors combinations with pomalidomide or lenalidomide and dexamethasone in multiple myeloma. Interim analysis of Keynote 183 and 185 revealed detrimental effects on overall survival (OS) with hazard ratios of 1.61 and 2.06, not explained by differences in toxicities alone. The checkmate 602 trial was also halted in light of these findings and also showed higher mortality in the nivolumab combination arm.
In the thoughtful NEJM piece, the authors make at least three important points. First, they question why these PD-1 inhibitors were tested in combination despite their having limited single-agent activity. In fact, a couple of years ago, Vinay Prasad and I asked the same question: why are novel cancer drugs being tested in combination despite having limited activity as a single agent? We found that these drugs, even when ultimately approved, provide relatively low value and recommended that drugs with poor single agent activity not be tested in combinations unless there are specific reasons to expect synergy.
The second important point in the article is that many cancer drug approvals are lately based on durable response rates in single arm trials without a control group, a situation in which it is difficult to evaluate the safety and efficacy of drug combinations. Indeed, without an RCT, the oncology community would never have known these signals of detrimental effect. If the FDA had approved these PD-1 inhibitors in multiple myeloma on the basis of non-randomized trials, which it often does in other oncology contexts, who knows how long it would have taken to recognize the increased mortality in patients—and at what cost. This is another reason why we need RCTs more now than ever. Finally, the authors point out that these PD-1 inhibitors in multiple myeloma were directly advanced to phase 3 trials after phase 1 trials were completed, without phase 2 information. Indeed, in a recent paper, Alfredo Addeo and I showed that a substantial percentage of drugs that fail in phase 3 trials do not have supporting phase 2 data. Continue reading…
In April 2016, I published guidance, in the form of a mock case study, on how to access a drug before it has been approved by the FDA—what’s known as pre-approval (or expanded or compassionate) access. This is an updated version of that guidance, reflecting multiple important changes in the pre-approval landscape over the past year. In particular, the FDA rolled out a new, streamlined form for single-patient requests, and Congress passed the 21st Century Cures Act, which, among many other things, mandated that certain pharmaceutical companies provide public information about their pre-approval access policies.
Patients (and physicians) trying to access an unapproved drug outside of a clinical trial can feel as though they’re navigating uncharted waters. Many physicians don’t know that the FDA permits the use of unapproved drugs outside of clinical trials; those who do know often have no idea how to access such drugs for their patients. Those physicians who know about pre-approval access are largely specialists in certain areas—often, oncology or rare diseases—and they are generally self-taught: they didn’t learn about pre-approval access in medical school or in their residencies. Thus, while some physicians have become very accustomed to requesting pre-approval access to drugs, the majority lacks this knowledge. In this essay, I use a fictional case to trace the process for requesting access to an unapproved drug. I hope to explode several myths about the process, especially the beliefs that the FDA is the primary decision-maker in granting access to unapproved drugs and that physicians must spend 100 hours or more completing pre-approval access paperwork.
Imagine you are a physician, and you have a pregnant patient who has tested positive for the Zika virus. She is only mildly ill, but she’s terrified that the virus, which has been linked with microcephaly and other abnormalities, will harm her unborn child. She’s so concerned that she is contemplating an abortion, even though she and her husband have been trying to have a child and were overjoyed to learn she was pregnant.
I never ceased to be amazed by how smart young clinicians solve problems that they see. Michelle Longmire was in residency at Stanford working with colleagues building point solutions when she realized that what they needed was an easy platform on which to develop medical grade apps. Her company Medable was the result. Then she realized that the other big market was clinical researchers, who now have access to Apple’s ResearchKit, but need an easy way to build a study without using developers. I interviewed her recently and she built a study for me using Medable’s new Axon product.
Maybe it is just the shock of being post Labor Day and realizing that summer is fading into the rear view mirror or maybe it was something I ate for breakfast that spurred new hope. But I think that this is the year that the patient centric approach to data in life sciences finally takes off. And along with that launch will come the massive rapid migration to cloud and data lake architectures for pharma data.
Really? Why now you may ask?
Yeah – that’s right. Every group I have been talking to is worried that they are sitting atop a jigsaw puzzle of siloed data resources that can’t be assembled fast enough to meet the needs of business and scientific users. Organizations are thinking that they can’t answer their questions about why drugs work in some patients and not others if they can’t link phenotype and genotype data. Groups can’t look across clinical trials. They can’t look beyond and between clinical trials and EMR data. Progressive safety groups are considering using automation and cognitive computing to lower costs in processing events so they can then look in parallel to expanding sensing new signals into 10X current volumes of data within large real world data sets.Continue reading…
Could computers develop the drugs of the future?The short answer: probably, but not yet.
Computer simulation is a cornerstone in the development and optimization of “mission-critical” elements in industries ranging from aerospace to finance.Even the smooth functioning of nuclear reactors – where failure would be catastrophic – relies on a computational model called a Virtual Reactor, which allows scientists and engineers to observing the reactor’s real-time response to operating conditions.
The analogous model in medicine – a “virtual human” – doesn’t yet exist.We still rely on living, breathing animals and humans to test drugs and devices.Discoveries are made largely by trial and error.But the age-old approach that led to the discovery of antibiotics, cardiac catheterization, and organ transplantation is becoming increasingly unsustainable.
The traditionally conducted clinical trial model requires increasing amounts of time, cost, and resources for both sponsors and sites. In fact, fewer than 10% of clinical trials are completed on time due to poor patient recruitment, retention and protracted budget negotiations. And since 2008 per-patient, clinical trial costs in the US have risen an average of 70% across all development phases.
In March 2015, however, BLOOMBERG BUSINESS
reported “Stanford University researchers were stunned when they awoke Tuesday 10 March to find that 11,000 people had signed up for a cardiovascular study using Apple Inc.’s ResearchKit, less than 24 hours after the iPhone tool was introduced. ‘To get 10,000 people enrolled in a medical study normally, it would take a year and 50 medical centers around the country’ said Alan Yeung, medical director of Stanford Cardiovascular Health. ‘That’s the power of the phone.”‘
At Scanadu, data collection and clinical studies are key to the development and deployment of our medical devices, and we’re fully aware of the kind of scale and speed and power we’re talking about here. And as a startup developing the next generation of medical devices for consumers, we had to innovate the clinical study process, while bucking the traditional assumptions of how a clinical study should operate.
I am a clinician and a clinical trialist. Medical research in some form or another (performing it, consuming it, reviewing it, editing it, etc.) occupies much of my time. Therefore, you can imagine my excitement while watching Apple’s product announcement yesterday when they introduced a new open source software platform called ResearchKit. Apple states ResearchKit could:
“revolutionize medical studies, potentially transforming medicine forever”
ResearchKit allows clinical researchers to have data about various diseases collected directly from a study participant’s iPhone (and perhaps other devices in the future — see below). The software is introduced as a solution to several important problems with current clinical studies, such as:
limited participation (the software allows everyone to participate; anyone with an iPhone can download a specific app for every study they want to participate in)
frequent data entry (patients can enter data as often as required/desired, rather than only at limited opportunities such as hospital or clinic visits)
data fidelity (currently-used paper patient “diaries” are prone to entering implausible or impossible values — the iPhone can limit the range of data entered)
Specifically, the website states:
ResearchKit simplifies recruiting and makes it easy for people to sign up for a study no matter where they live in the world. The end result? A much larger and more varied study group, which provides a more useful representation of the population.
This is a bold claim. We’ll see below that it doesn’t yet ring true.