Is it greed or living in the past that holds back the clinical trial industry?

Well – it certainly isn’t science and technology innovation or even the FDA.At a recent executive roundtable, according to Tufts University’s Center for the Study of Drug Development director and associate professor Ken Getz, it is that many pharma corporations and CROs have not yet fully embraced the newest, most efficient technologies, and this has been a huge problem in recent years.

More expensive. Riskier than ever.

“Drug development cycle times have not gotten faster, costs continue to increase, and drug development has become riskier than ever with only 11.8% of products that enter clinical testing receiving regulatory approval, about half the rate of the 1990s,” says Getz.

Which, of course, is true, as we in the clinical trial industry know that it takes about a decade for a drug to be developed and on average seven years for it to be tested before it makes it to market, and only if it gains regulatory approval. So, what would happen if clinical testing time could be shortened?


Managing change in medtech clinical trials

Jenya wrote a piece about change management for regulatory folks who work with medtech geeks and I just had to write my own intro.

Frankly, its easier to talk about change for other people than for yourself. A lot easier.  I have written here, here and here about the gaps between the myriad of stakeholders in clinical trials – security, IT, engineering, product marketing ,regulatory affairs and medical device security to name a few.

Change management is a topic usually overlooked when medtech companies implement cloud EDC and electronic source documents for their connected device/mobile medical device/mobile medical app clinical trial.

In this post, Jenya talks about how to manage change during the transition from traditional clinical trial data management to cloud technologies, remote monitoring and electronic source data.

So enjoy.



Call me when you have a nickel in your pocket

There is a saying in American English dating back to the 1940’s – “Call me when you have a nickel in your pocket”. With limited budgets, small, innovative medical device vendors will be looking at the nickel in their pocket and thinking that they cannot afford a cloud EDC offering and opt for the paper default or the DIY option for monitoring. None of which are a particular good idea.


The key is not first to eCRF the key is smart to market

This post is not for the Pfizers, Novartis, Merck and GSK giants of the life science industry.

Its for the innovators, the smaller, creative life science companies that are challenged by the costs, the regulatory load and complexity of executing a clinical trial.

This post is dedicated to the startup entrepreneurs of the world.

Building an EDC system for your clinical trial requires executing a plan in order to successfully recruit patients, collect high quality data, sustain patient safety and produce your statistical report in a timely fashion. You can potentially embark on an EDC journey without a plan, without a simple, well-designed protocol, and without appropriate clinical monitoring. This will guarantee you a long trek of pain, burning cash while you resolve issues and clean data.

The pivotal question to any clinical decision maker is this: Do you want to start building an ECRF (electronic case report form) now and pay in pain and cash later, or plan now and own the process?

Simple concept, but important message.

It doesn’t matter if your business is a one-person startup or a “Big Five” bio-technology company. If you develop medical devices, medtech, biotech or drugs on a daily basis, you are faced with an increasing stable of competitors, and barriers to success that can frustrate you as a business manager or a startup entrepreneur trying to make payroll.

Being an entrepreneur like you, I’ve constantly been exposed to walls that have continuously tried to prevent me from success. In this post, you will learn how to plan and execute EDC quickly, efficiently and successfully and break through the business, clinical and regulatory barriers that stand in your way. In a world where competition erodes market share, depresses product pricing, and where large company branding and marketing tramples the innovative medtech startup, the key is not first to eCRF the key is smart to market.

So – here are 2 factors to consider to help get you faster to the finish line.


Assuring patient compliance to the study protocol-spending smart on monitoring

Today I want to go beyond having compelling ideas, a team and a great market opportunity and talk about what you need to successfully execute a clinical trial on your way to FDA approval.

Sometimes there is nothing more powerful than the passion and vision of an entrepreneur.

But passion and vision are just not enough. You need execution. You need to be able to accept the pain.

Execution for an early stage biomed startup means successful execution of clinical trials, from pilot through double-blind Phase I to Phase II and Phase IIB validating the efficacy and safety of your product.

Are you overly optimistic about the time it will take to get results from your study?

In our experience, even experienced entrepreneurs do not factor in the amount of time it really takes to collect data in the clinical trial and monitor and assure patient compliance.

Patient compliance to your protocol is by far the most important success factor for interventional trials. It is the basis for everything and the key to your time and money.

There are 2 schools of thought on the topic of assuring protocol compliance.

The first school of thought likes outsourcing everything to a CRO.

This is often an expensive proposition which does not assure optimising time to market for a very good reason: the CRO business model is not success-based.


Assessing and assuring high quality dates in clinical trials

Bad Dates: Assessing and assuring high quality dates in clinical trials


Clinical trials are based on collections of time-based clinical data. If the dates and time-stamps in the data set are low quality, everything else will be low quality: measurement of study progress, enforcement of visit protocols and study schedules, measurement of site progress and any clinical parameter that is a function of time, such as cumulative dosing, pregnancy and hundreds of other time-based use cases.

Jenya talks about bad dates and how really bad quality dates that can spell disaster for your clinical trial – and suggests what do to about it.


The LA Freeway model of study monitoring

A freeway paradigm helps explain why onsite visits by study monitors don’t work and helps us plan and implement an effective system for protocol compliance monitoring of all sites, all data, all the time that saves time and money.

But first – let’s consider some  special aspects of clinical trial data:

Clinical trial data is highly dimensional data.

Clinical trial data is not “big data” but it is highly-dimensional in terms of variables or features of a particular subject.

Highly dimensional data is often found in biology;  a common example of highly dimensional data in biology is gene sequencer output. There are often tens of thousands of genes (features), but only tens of hundreds of samples.

In a clinical trial, there may be thousands of features but only tens of subjects.

Traditional protocol compliance monitoring uses on-site visits  and SDV  (source document verification) that requires visual processing of the information at the “scene”.   Since the amount of visual information available at the scene is enormous,  a person processes only a subset of the scene.

Humans focus on the interesting facets of a scene ignoring the rest. This is explained by selective attention theory.

Selective attention.

Selective attention is a cognitive process in which a person attends to one or a few sensory inputs while ignoring the other ones. Selective attention can be likened to the manner by which a bottleneck restricts the flow rate of a fluid. The bottleneck doesn’t allow the fluid to enter into the body of the bottle all at once; rather, it lets the fluid to enter in certain amounts depending on the flow rate, until all of it has entered the bottle’s body. Selective attention is necessary for us to attend consciously to sensory stimuli in such a way that we will not experience sensory overload. See the article in Wikipedia on Attenuation theory.


Why merging medical records, hospital reports, and clinical trial data is a very bad idea

In this outstanding guest post, security and privacy expert, Veronika Valdova from Arete-Zoe explains why merging medical records, hospital reports, and clinical trial data is a very bad idea.

 Data breaches endanger your clinical trial success

Medical privacy and breaches of personal health information (PHI) has been a hot topic for several years. For the clinical trial industry, the main concerns are decline in recruitment resulting from lack of confidence in data handling and instances of breaches that affect data integrity that adversely affect NDA and MA applications in major markets, which precipitates administrative action taken by national regulators in response to local incidents.


An attack modeling approach to clinical trial risk analysis

an attack modeling approach to clinical trial risk analysis
What does taking off your shoes and belt in the airport have in common with risk assessment in clinical trials?

Today we talk about the drawbacks of traditional risk assessment and propose an alternative approach to clinical trial risk assessment that is based on data and considering plausible attacks on your trial as opposed to fixed protocols and human monitoring processes.


Why paper is better than cloud technology for your clinical trial

6 reasons why you should use paper CRF in your clinical trials

You’ve been considering cloud EDC for your next clinical trial, but you are put off by the perceived challenges and costs of implementation and operation. You shudder at the thought of DIY EDC solutions like Clincapture or Medrio and don’t have the budget for Medidata.

For many early stage medtech startups, the first feasibility trial may involve only 1 or 2 sites and a relatively small number of patients.    You would nominally assume that a paper CRF is cheaper and less trouble than cloud EDC.

So how can a smart CEO of an innovative medtech or drug startup make a smart decision for data management?