Pharma’s Cutting Edge

NEJM — Detection of Mutations in EGFR in Circulating Lung-Cancer Cells (Sub Req)

Look to history for a sense of the import of today’s medical advances. 

Most authorities recognize TR Ashworth, writing in the Australian Medical Jorunal, as making the first discovery of cells in the blood similar to those in patients’ solid tumors (post-mortem).  That was in 1869.  I couldn’t find the original article online, but the citation is: [Ashworth TR (1869) A case of cancer in which cells similar to those in the tumours were seen in the blood after death. Aus Med J 14: 146–149, as referenced in this minireview.]

Surely, shortly after word of this discovery spread, scientists began pondering ways of exploiting this knowledge to diagnose and track the progression of cancer.  But a huge technological gulf separated the idea of using circulating tumor cells for the benefit of cancer patients and its application in the clinic.  As you might imagine, the number of circulating tumor cells (CTCs) relative to the total number of circulating blood cells is relatively small, necessitating reliable tumor cell-enrichment and detection technologies to make diagnostic/theranostic use of CTCs.

Fast forward some 135 years from Ashworth’s initial discovery to the routine use of techniques to exploit it.  In January 2004, Veridex, a J&J company, received US FDA approval to market CellSearch, a device for magnetically labeling antibody-selected epithelial cells in a blood sample, for use in patients with metastatic breast cancer as an aid to monitoring response to treatment.  The theory behind the device and accompanying analysis of the BRCA CTCs is that the number of CTCs correlates (inversely) with response to treatment.  To my knowledge, this is the only such separation/analysis system available in the U.S. for non-experimental monitoring of response to therapy via CTCs.  It has now been approved for marketing for metastatic colon and prostate cancer in addition to BRCA.

Unfortunately for Veridex and for patients, the system has not been shown to affect outcomes in metastatic breast cancer, where it has been most thoroughly studied, leading ASCO to make the following practice recommendation:

The measurement of circulating tumor cells (CTCs) should not be used to make the diagnosis of breast cancer or to influence any treatment decisions in patients with breast cancer. Similarly, the use of the recently US Food and Drug Administration–cleared test for CTCs (CellSearch Assay; Veridex, Warren, NJ) in patients with metastatic breast cancer cannot be recommended until additional validation confirms the clinical value of this test.

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“Always do whatever’s next”  –  George Carlin (RIP)

I popped into the DIA Annual Mtg. for a day this week to chair a session on content reuse in clinical regulatory documents.   Good turnout for the session and, as usual, for the meeting, which continues to be the one industry get-together that clinical-research worker bees are paid to attend.

The exhibitor hall was dominated by full-service CROs touting their capabilities.  Have you noticed that you never hear much from the partial-service CROs?  Boutique CROs were there, but not the partial-service CROs.  The boutiques are just like the partial-service providers, except they offer clients tea while they’re waiting for service to begin.  Okay, enough silliness.

For about a year, up until the end of this April, I was working for what was Fast Track Systems Inc. and what is now a part of Medidata Solutions Worldwide.  Medidata made a smart purchase.  Medidata’s Designer software is the future for structuring information at the head-end of the clinical research value chain.  Structure information while you’re writing a protocol in Word, and then feed all your dependent documents and systems with it, all without losing the original meaning and intent of the protocol authors.  Makes perfect sense, huh?  Medidata also inherited Fast Track’s successful investigator grant and CRO contract benchmarking data/software as part of the deal.

Anyway, I’m now gainfully self-employed.  That is to say I was repositioned out the door following a corporate restructuring.   If you work in pharma, odds are you or someone you know has been through a similar “restructuring”.  Fun…ain’t it? 

I’m available as a consultant, advisor, guru, confidant, or secret friend.  You can look at my consulting website (pharmagrowth.com) to read about me, or just drop me a note [fred at pharmagrowth dot com].  I’m always glad to hear from my readers!

Above: NMR solution structure of exenatide from Ganfyd.org

 taspoglutide advancement announcement

Lead 6 results press release

52-week data from Duration-1 trial

A bit more excitement than usual surrounded this year’s recently completed ADA annual meeting, with a flurry of news surrounding the GLP-1 mimetics and their brethren, the DPP-IV inhibitors.

In my mind, the most interesting bit of news concerned progress on so-called exenatide LAR, a once-weekly formulation for sc injection of Amylin/Lilly exenatide.  According to Amylin’s website, the LAR program is being jointly managed by Amylin/Lilly.  However, Amylin hosted an Amylin-only event at ADA to discuss the development program, leading me to speculate that Lilly’s role in the drug’s development is strictly advisory/financial.  If you know otherwise, do chime in.

Regardless of whose pulling the strings, the program has progressed well into Phase 3clinical development, and Amylin shared the 30- and 52-week results from the first (Duration-1; see above link) of the large Phase 3 studies at the meeting.  I’ve supervised and reviewed lots of clinical trials of diabetes drugs, and I can tell you that the results with exenatide LAR thus far are potentially game-changing.

Sure, there’s a long way to go until registration:  more Phase 3 studies, including head to head with DPP-IV inhibitors, and a bridging program from the research formulation to the commercial form.  Amylin knows this; its management team strikes me as completely realistic and credible regarding the difficult tasks ahead.  I also appreciate the lack of hyperbole and cheerleading we used to get from the Ginger Graham-led team.  There’s no need for these distractions when your drug does the PR for you.

Let’s look at some of the drug characteristics and Phase 3 data that make me bullish about exenatide LAR’s potential impact on type 2 diabetes:

1.9% HbA1c drop from baseline with average BL A1c in low 8’s; an improvement compared with Byetta, although the improvement appears to be confined to those with worse control at baseline.  This is probably due to the better effect on FPG in the high-A1c subgroup.  Presumably high FPG drives the higher A1c, and the LAR is able to suppress overnight FPG better than regular exenatide. 

The A1c drop was extended to 52 weeks in the extension study, which offered LAR to all participants, indicating durability of effect to 1 year.  Many other therapies start to show waning effect on A1c at 1 year.

Both FPG and PPG were suppressed, and the PPG suppression appeared more “physiologic” than with regular exenatide.

Weight loss and cholesterol/SBP reduction were similar to regular exenatide; lipid effect maybe a bit stronger.

Well-tolerated with less nausea than regular exenatide.

Once weekly administration, with 94% of the injections self-administered in the clinical trial (i.e. easy to self-administer after brief education).  Issues regarding formulation consistsency will be worked out before launch.

Compliance likely to be at least as good as Byetta (twice daily exenatide), despite presumably larger gauge needle with higher rate of injection site symptoms (primarily itching), due to the need to inject only once a week.

Once weekly injections will make the drug attractive to somewhat better-controlled patients, for whom injections are rejected as an option now.  How much more attractive awaits real-world experience, but I speculate–an educated guess–that the acceptance will be much greater than either insulin or a once-daily formulation of GLP-1 analog.  Still, I think the role is later in the disease right now, with the DPP-IV drugs dominating the early/better-controlled patient group, along with the other oral meds. 

What I think we’ll see with LAR in the marketplace (whenever that may be; we should know more by the end of this year) is that the average duration of diabetes and average A1c prior to therapy will both drop substantially relative to Byetta within a year of the LAR’s launch.  Eventually, LAR should totally supplant Byetta, and also steal some of the volume share still held by the SUs, the TZD’s, insulins, and some held by the DPP-IVs.  Like I said–game-changing.

Meanwhile, as I teased above Novo is looking like it has a real drug with its once-daily liraglutide, potentially a strong competitor to Byetta.  But remember that liraglutide is an NME; it’s got a scaredy-cat FDA in the US and a diabetes NME-unfriendly EMEA in Europe it must face before selling a thing.  Once it makes its way onto the major markets, exenatide LAR will be breathing down its neck.  Liraglutide may be able to compete for Byetta patients, but I don’t see how once daily therapy can compete with once-weekly, especially when the API in the once-weekly has an enormous amount of patient experience and clinical research behind it.

As for Ipsen/Roche (see above linked press release), they’re pressing forward for now.  They’ve really got their work cut out for them.  A straightforward “me-too” type of development program is unlikely to reap them the ROI they seek.  They’ll need to be creative in Phase 3, and focus on the unmet medical and practical needs that (might) remain after exenatide LAR hits the market. 

Finally an editorial plea, in light of the recent ACCORD and ADVANCE trial results, which suggest that drugs that carry a risk of hypoglycemia are likely not appropriate for strict glycemic control in advanced type 2 diabetes, I urge Lilly and Amylin to provide financial support for a large study of Exenatide LAR with cardiovascular endpoints to be started as soon as feasible.  It is likely our nearest best hope to reduce the risk of CV events (or at least not increase them) in older diabetics, while maintaining glycemia close to normal to prevent microvascular disease.  US government support for such a study is realistic. 

In the June 2nd paper issue of BusinessWeek (published online 5/21) the article “Cancer’s Cruel Economics” by Catherine Arnst provides a high-level look at the difficulty some small copmpanies are facing getting their cancer therapies approved in the US. 

The focus is on cancer immunotherapies, particularly Antigenics’ Oncophage.  I last discussed Oncophage in 2006, after the first report of its Phase 3 results.  I have also devoted space in this forum to other cancer therapies mentioned in the BusinessWeek article including Dendreon’s Provenge and Genitope’s MyVax.

I was intrigued by a quote attributed to Richard Pazdur, head of CDER’s Oncology review division:

“[Post hoc subgroup analysis for differential treatment effect] is like shooting an arrow and then painting the bull’s-eye around it,” says Pazdur. “You cannot use subset analysis to salvage a failed trial.”

Pazdur’s concern regarding treatment effect inferences derived from post hoc subgroup (subset) analyses rests on firm grounds, but the quote suggests a black and white attitude towards their utility, without any room for compromise.  That’s too bad, because the rule of thumb Pazdur is apparently using to reject subgroup evidence of efficacy is imperfect, undoubtedly resulting in the rejection of some effective therapies.

I’m not going to write a manuscript-length post describing the many risks inherent in inferential subgroup analyses.  There are many published reviews you can find that do that.  Suffice to say that the risks of both false-negative and false-positive inferences are inflated with subgroup analyses relative to the main analysis (primary hypothesis test), whether the analyses are pre hoc (defined before the trial results accrue) or post hoc (sometimes called retrospective).  Pre hoc analyses are less susceptible to Pazdur’s target drawing, especially when the specifics of the subgroup are rigorously pre-defined than post hoc, and so they are preferred by regulators.

What I’ve found to be less well represented in the literature is a situation in which the weight of evidence presented in the subgroup analysis is sufficient to, as Pazdur says, “rescue a failed study.”  I’ll not focus specifically on Provenge or Oncophage, but the example from the literature I’ll cite is relevant to both.

In order to determine whether any subgroup analyses provide evidence sufficient to warrant drug approval, it is necessary to first know the expected false-positive rate of a subgroup analysis, given a false-positive rate of 5% in the overall (main) analysis.  A 5% rate is chosen, because that rate is usually considered an acceptable one by clinical practitioners and drug regulators.  Thus, the null hypothesis is rejected falsely in 1 in 20 trials.  FDA usually requires two independent experiments (trials) for evidence of efficacy, resulting in an overall false-positive rate of 2.5% (0.05×0.05), though one statistically significant experiment with corroborating evidence from others is sometimes sufficient, particularly for accelerated approvals.

In an important study published in 2001 by the UK’s NHS, Brookes et al used simulations of 100,000 clinical trials each to determine the false-positive (and false-negative) rates of subgroup analyses for different types of study designs.  They simulated two subgroup analysis (ignoring the effects of multiple analyses, which inflate Type 1 error) and tested a variety of relative treatment effect and subgroup sizes.

The simulations showed that when there was in reality no main or subgroup effect of treatment, and the overall (main) analysis of treatment was falsely positive (i.e. null hypothesis was rejected at the nominal p<0.05) then the chance of falsely declaring one subgroup as demonstrating a treatment effect was high.  For a survival study, this chance was 61%.  In other words, with no real main treatment or subgroup effect, when there was a false-positive main effect, one of two subgroups analyzed will appear to have a treatment effect well over half the time. 

However, under the same set of circumstances, when the main effect is not rejected (i.e. a true negative inference is made), then one subgroup will show evidence of a treatment effect much less often, only 6.5% of the time, approaching the overall effect false-positive rate of 5%.  In other words, the probability of falsely rejecting a subgroup-specific null hypothesis in the absence of overall and subgroup effects is reasonably low if the overall effect is correctly negative.

Of course, the above simulation findings aren’t by themselves capable of determining whether a subgroup-specific effect is real or not.  They simply suggest that the regulator need not reject out-of-hand statistical evidence of a subgroup-differential treatment effect when evidence for an overall effect is absent, as Dr. Pazdur’s quote suggests he is willing to do in some cases. 

Evidence that the apparent subgroup effect in a survival study is real will be strengthened by the following factors:

• The main effect does not contradict the purported subgroup effect 
• The subgroup-specific analysis was defined a priori
• A significant test of interaction between overall treatment effect and the subgroup is in evidence prior to any subgroup-specific test
• The total number of subgroups analyzed is small, and, if not, an inference of treatment effect made on any one subgroup uses an appropriately conservative adjustment of the significance level
• There is strong biological plausibility for the differential subgroup effect
• The size of the subgroup is large relative to the total sample size (i.e. relatively representative of the total population)
• The conduct of the study, particularly the handling of dropouts and non-compliant subjects, creates confidence in the quality of the subgroup data

Finally, as I’ve argued before in the case of Provenge, when the evidence of efficacy is marginal, regulators have a duty to the public they serve to weigh with utmost care and without bias the risk of introducing an ineffective medicine versus the risk of withholding ready availability of an effective medicine from a gravely ill population without other treatment options. 

In December 2006, I described my initial impressions of Google Patent Search beta, a free USPTO patent search engine, powered by Google.  As you’ll read, I wasn’t thrilled with this early release.

So, how has Google fared with some time–nearly 17 months–to marinate its patent search engine?

First off, Google hasn’t promoted the service out of beta yet.  I don’t know how Google decides to promote a new tool to production, but I have to think that many thousands of searches have been made with it since beta testing began, and they’ve had plenty of time to work out the kinks.  Beta schmeta…is it good enough to recommend?

Once again, I’ll compare Google Patent Search directly to the USPTO search engine, as Google has not yet added international patents. 

The Google search interface has been improved and provides simple form field searching for patent number, inventor, assignee, classification code (US and international), issue date, and filing date.  Google’s advanced search operators also work, including the “-” for NOT and “OR”.

The USPTO quick search allows search of all these fields plus some two dozen more, and their advanced search interface allows all of these delimiters to be accessed simultaneously, albeit using a complex syntax.  If you’re doing serious patent research, Google’s search interface will simply not provide you with enough search specificity to meet your needs.

For simple searches, the USPTO quick search and Google Patent advanced search interfaces are similar in user-friendliness and utility.  (The basic Google Patent search has very limited utility.)  So, the key differentiators will be result accuracy and result presentation.

Last time I looked at the Google service, it offered results in a list sorted by relevance-according-to-Google.  In my mind, this made the service virtually useless for industry-based searchers.  Google has since added in a date sorting.  I would have preferred having a patent number sorting option, but date at least makes the search worth doing.

As for relevance, I’m still scratching my head with Google.  It seems to return results in some quasi-random way.  For instance, search the exact phrase (in quotes) “Eli Lilly” the field assignee name, using the entire USPTO issued patent database (quick search) or Google Patent Search (advanced search).  The USPTO returns 3,793 hits dating back to 1971.  Google returns only 527 issued patents dated to 1976, and very strangely, only 461 filings of any status (issued plus applications combined–yes this number is lower than issued patnets alone!), and just 46 patent applications (USPTO returns 94 applications).

Clearly, there is something amiss with Google’s Patent Search.  It really does behave as a beta product, despite its protracted time in use.   It has some potential advantages to the USPTO search in theory, claiming, for example, full-content access to all US issued patents, whereas the USPTO offers full-text access only as far back as 1976.  But it remains fatally flawed for all research purposes except cherry-picking individual patents by number, when you are certain the patent number is correct.