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Winter Newsletter 2019

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Kinexions Winter 2019 Newsletter
 
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Thomas Seoh
 
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Dear Friends of Kinexum,   

Welcome to the Winter 2019 edition of Kinexions, the Kinexum newsletter!  I begin with some great news, and then some sad news. 

Featured in this issue are five Kinexum and guest authors:
(i) Sue Manley, Kinexum Project Manager and former Global Head of Regulatory Operations for Novartis, discusses considerations for small to mid-sized companies planning to file an NDA;
(ii) Tom Hedberg, PhD, a Kinexum senior medical writer and Executive Director of the International Medical Crisis Response Alliance (IMCRA), details the emerging silent epidemic of diabetes in China;
(iii) Dave Bergstrom, PhD, Kinexum strategic formulations and sourcing expert, outlines strategies for approaching CMC, new product development, and sourcing;
(iv) Michael Cobb, software engineer of analytics software startup Navya Network, describes an application of machine learning in healthcare in the form of scalable treatment solutions for cancer diagnoses; and
(v) Jennifer Zhao, Kinexum Associate and recent graduate of Dartmouth, recaps Kinexum’s October webinar on the Targeting Aging with Metformin (TAME) clinical trial that is pioneering a regulatory pathway with FDA for multiple diseases of aging.


 
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Guest Authors                  

 
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Early NDA Planning for Small to Mid-sized Companies

Sue Manley
The final phase 3 studies are ongoing with a planned completion in 12 to 18 months. Your board is looking to the submission date with great excitement. Your company has made it this far – is time to relax?

No, not yet! Your company has not prepared a US NDA in several years or, even more concerning, has never prepared an NDA.

What can you do now to ensure a smooth trip to the submission and beyond?
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Strategies to CMC New Product Development and Manufacturing Outsourcing Resources

David Bergstrom, PhD
If your company is developing a new product, your team will—at some point—have to consider a CMC (Chemistry, Manufacturing & Controls) and outsourcing strategy. How should your company develop such a strategy?
 
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Zan Fleming, MD
 
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Staying on the NDA Learning Curve                      

I was once—literally—the poster child for New Drug Application (NDA) review at FDA.
 
I arrived at FDA in 1986, thinking my time there would be a sabbatical from NIH. Shortly after my arrival, the editor of the FDA Consumer Magazine asked if I would appear in an article about NDA review at the Agency. I agreed. Magazine staff then set me down in the document room of my division on the 14th floor of the Parklawn Building...
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Zan Fleming, MD, and David Klonoff, MD
Zan Fleming, MD, with Diabetes Technology Society (DTS) founder
David Klonoff, MD, at the 2018
Diabetes Technology Meeting (DTM)
Zan Fleming, MD, and Jisun Yi, MD
Zan Fleming, MD, moderating the
"Novel Hardware: Implanted Sensors" panel at DTM 2018 with
FDA Medical Officer Jisun Yi, MD
 
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The Silent Epidemic of Diabetes in China

Thomas Hedberg, PhD
There are frequently heavy prices to pay for cultures embracing the lifestyle of Western nations, particularly of the US. China’s rapid economic and social expansion in the past 30 years has led to unwanted and heretofore unknown health consequences. The first epidemiological investigation of diabetes in China in 1980 showed a diabetes prevalence of 1%. By 2009, however, that prevalence had grown to 10%, and diabetes has since become the third largest chronic disease in China after cancer and cardiovascular pathologies [1]...
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Applications of Machine Learning in Healthcare: Scalable Treatment Solutions for Cancer Patients

Michael Cobb
A decade ago, Ms. Gitika Srivastava and Dr. Naresh Ramarajan learned that their close relatives had been diagnosed with cancer. In response, the two co-founded a company that uses machine learning to transform how patients make cancer treatment decisions...
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Kinexum Webcast Recap: Using Metformin to Target Aging

Jennifer Zhao
On October 26, 2018, Kinexum hosted a public webcast featuring the co-principal investigators, Steve Kritchevsky, PhD, and Nir Barzilai, MD, of the upcoming Targeting Aging with Metformin (TAME) clinical trial. Dr. Kritchevsky is Director of the J. Paul Sticht Center for Healthy Aging and Alzheimer’s Prevention at the Wake Forest School of Medicine. Dr. Barzilai is Director of the Institute for Aging Research at the Albert Einstein College of Medicine. Metformin holds a special significance for Kinexum...
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Continuation of Above Articles...

 

Note from Kinexum CEO (cont.)

We are very excited to announce our Metabesity 2019 congress on October 15-16, 2019 at the Carnegie Institution for Science in Washington, DC.  Building on the great success of Metabesity 2017 in London, we are assembling another stellar roster of speakers, including Victor Dzau, President of the National Academy of Medicine, Richard Hodes, Director of the National Institute of Aging, and Janet Woodcock, Director of the FDA Center for Drug Evaluation and Research.  Updates will appear in future Kinexionsissues.

Please join us for the next Kinexum public webcast on Friday, December 14, 2018, from 11 a.m. to noon, EST, by Tom Hedberg, PhD, on the causes, effects, and global aspects of post-traumatic stress disorder (PTSD) and responses to current treatment failure; please register here.  

If you missed our previous webinar on the TAME clinical trial, you can view a YouTube video here.

I highly recommend to all our emerging company clients an upcoming webinar by our colleagues at Cello Health BioConsulting (previously Defined Health), "The Rock Just Below the Water: Market Access Risk and Early-Stage Biotechs," on the criticality, earlier and earlier, for emerging life science companies to demonstrate value to payers. The free webcast will be moderated by Ed Saltzman, Executive Chairman of Cello Health BioConsulting, and a heavyweight panel of Roger Longman, Chairman, and Jeffrey Berkowitz, CEO and Director, of Real Endpoints, and Dennis Purcell, Founder and Senior Advisor of Aisling Capital. The webinar will be broadcast on Friday, December 14, 2018, from 12:45 p.m. to 2 p.m., EST.  Please register here.

Kinexum will be in San Francisco during the upcoming JP Morgan Healthcare Conference, January 6-10, 2019, including at the RESI conference. If you would like to meet to discuss matters such as regulatory or clinical development strategy or negotiations with or a submission to FDA or another regulatory authority, please email  This email address is being protected from spambots. You need JavaScript enabled to view it.  to make an appointment.  

Wishing you a happy, healthy and fulfilling holiday season and 2019! 

Cheers,
Thomas
 
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From Kinexum Founder: Staying on the NDA Learning Curve (con't)

 
They took my picture: a young tike perusing a 4-inch volume from an NDA, with 500 volumes in the background. I don’t recall if these volumes were actually an NDA that I reviewed, but they could have been the lovastatin NDA. By sheer luck, I was handed lovastatin as my first NDA review a few weeks after joining the Agency. Also lucky for me, this first-in-class statin generated much excitement and attention in the press. Statins went on to become one the most important drug classes for chronic diseases ever and led to Nobel prizes for Brown and Goldstein.
 
Being very green, I did not know any better than to put my shoulder to the lovastatin review. I also encouraged my colleagues to hurry up on their reviews, some of whom were rather perturbed by my pushiness. The result was a quantum leap in the speed of NDA review. Lovastatin was approved in 11 months—the average time to NDA approval at the time was over 2.5 years. CDER Director Carl Peck in an all-hands meeting raised lovastatinas the model for NDA review. All of this led to many other unmerited opportunities and adventures at FDA. 
 
At the time of my first NDA review, I thought I was on the steepest learning curve that I would ever face. The torrent of all the principles and practices of therapeutic evaluation made my training and early research in metabolism and endocrinology seem trivial.
 
Learning curves for us all have only gotten steeper, which is both good and bad. They’re good for making life exuberantly exciting, but they’re bad for keeping us awake at night recounting how many desperately important articles and books had been left unopened on the table.  
 
Sue Manley’s article on NDA planning, featured in this issue of Kinexions, reminds me how I have fallen off the learning curve for compiling NDAs. Although I am frequently involved in NDA design, preparation, and prosecution, I have fallen off the curve in understanding today’s process of building an NDA, which has dramatically changed over the past three decades. One difference is the sheer volume of data and narratives involved. The 500 volumes of the lovastatin NDA could be put on a $4 memory stick, but a multi-terabyte hard drive is required to accommodate today’s typical NDA. Another difference is the standards for clinical and nonclinical reviews. Although the current digitalized submission and reviewing system facilitate NDA review at FDA, the expectations for data handling, document management, analysis, and interpretation have exponentially increased. My 80-page lovastatin review would not make the cut by today’s standards.
 
Although old-timers like myself have fallen off some learning curves, this doesn’t mean we have to give up. Instead, we should strive to remain on the learning curves of the areas on which we continue to focus. 
 
Sue and I started our professional careers at about the same time, though we were in different roles: I in clinical evaluation, she in regulatory and project management – an equally challenging ballpark. Susan exemplifies someone who has maintained her craft in the guild of NDA builders. She has stayed on the still-steep learning curve for NDA design and construction, an enterprise comparable to building a cathedral. My hat is off to her and other craftsmen who have stayed on that learning curve. 

Written by Zan Fleming, MD, Kinexum Executive Chairman
 
 
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Early NDA Planning for Small to Mid-sized Companies (cont.) 

 
As a first step, familiarize yourself with eCTD, which is the standard electronic organization and format that is required for a US NDA (see Brandon Jones’s Spring 2018 article for a brief overview). Establish a high-level eCTD timeline, section by section, to identify the critical path to the submission date. List the individual components with the planned completion date, then examine each component/completion date pair to determine dependencies and whether the component completion can be accelerated. This timeline should be used as the basis of a tracking sheet that specifies resources and completion dates. The level of detail needed for the tracking sheet depends on your organization. For example, if you have a robust clinical organization, you may delegate the detail of clinical study completion and report preparation to that group, while you only track the study report completion date. Of course, the items on the critical path will need to be carefully staffed and managed.
 
In order to complete NDA submission, smaller organizations often need contract staff to deal with the peak workload during the last year before submission. A critical strategy is to bring key contract staff on board early enough so they become familiar with the drug class, planned documents, key scientific and regulatory issues, and internal procedures. If this strategy is not implemented, several setbacks may arise. Too often has the situation occurred in which more medical writers are added at the last minute without proper time and attention to orientation. This rush usually results in senior document reviewer frustration with document content that can involve multiple, time-consuming draft cycles.  
 
The last item typically finalized is the product labeling. The last components that feed into the labeling are the clinical summaries: the Integrated Summary of Safety, the Integrated Summary of Efficacy, 2.7.3 the Summary of Clinical Efficacy, 2.7.4 the Summary of Clinical Safety, and 2.5 the Clinical Overview (which includes a key discussion of risks and benefits). These final items deserve careful attention and review.  
 
The NDA submission requires labeling components to be annotated to the source information. A good practice is to prepare and maintain an “emerging package insert” during development, rather than drafting the label at the end of development. This emerging package insert should be annotated with the planned source document and any relevant comments. As work progresses, the emerging package insert will be modified based on development findings.
 
Careful planning should also apply to the order in which individual modules of the NDA are completed. Module 1 (Administrative Information), Module 2 (Summaries), and Module 5 (Clinical Study Reports) are usually the last sections to be completed. Frequently, Module 3 (Quality) and Module 4 (Nonclinical Study Reports) can be completed and provided to e-publishers days or even months before the completion of the other sections. A best practice is to complete Module 3 and 4 as early as possible. If this practice seems feasible for your company, you should consider discussing a rolling submission with FDA.   
 
Other topics to be addressed early on include:
  • Timing and content of the pre-NDA meeting. A draft package insert is required for this meeting. The planning and timelines for the NDA preparation should allow flexibility for revisions based on the outcome of this meeting.  
  • Organization of team meetings to communicate the “storyline” and address critical issues based on internal knowledge and FDA communications. 
  • Definition of writing standards, such as product name, document headers, use of templates, and granularity of documents. These standards will facilitate eCTD writing and future updating. 
  • Specification of the process for completing submission components, including quality control, review, sign-off, and storage. Storage should include not only the PDFs for submission, but also the final Word files, for the Word files will be helpful for future NDA amendments or submissions to other health authorities.
All trips to NDA submission have a few traffic humps and even some pot holes. Planning early while keeping these strategies and best practices in mind will help you navigate the journey more easily.

Written by Sue Manley, Kinexum Project Management/Regulatory 
 
 
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The Silent Epidemic of Diabetes in China (cont.)

 
According to the International Diabetes Federation, while diabetes is skyrocketing in China, the nation is still largely unprepared for the expected increase in diabetes-related comorbidities and complications. Fortunately, these complications have not yet become as widespread as the core disorder.

According to the CND-MDSG (China National Diabetes and Metabolic Disorders Study Group), in a 2010 NEJM article titled “Prevalence of Diabetes among Men and Women in China,” 92.4 million Chinese adults have diabetes and 148.2 million may be classified as pre-diabetic [2]. As of 2017, the number of diagnosed cases has jumped to 113.9 million [3]. These numbers will continue to grow as the population further embraces the fundamental changes that accompany economic development and urbanization. Among these, perhaps the most unhealthful is the adoption of a considerably more sedentary lifestyle and a fast-food driven diet. According to the Chinese Diabetes Society, the incidence of diabetes will grow substantially over the next 10-20 years to a point where approximately 50 million Chinese with undiagnosed diabetes will begin seeking medical care [4]. With Chinese diabetics currently constituting nearly a third of all cases worldwide, the World Health Organization (WHO) projects that diabetes will be the seventh-leading cause of death in China by 2030. An ongoing JAMA study, conducted in collaboration with the Chinese Society of Diabetes (CDS) and the International Diabetes Federation (IDF), has determined that the annual cost to the Chinese national health service for treating diabetes was 173.4 billion Renminbi ($28.5 billion US), or ~13% of the total national expenditure for health services [5]. The growth rate of pediatric diabetes in particular was recently heralded by an alarming declaration recently made by Novo Nordisk: “…increasing childhood obesity in China is to diabetes and chronic diseases what melting glaciers are to climate change: a warning signal of times to come…” Clearly, the problem is epidemic and calls for an early, comprehensive, and definitive therapeutic approach.
 
Recognition and Challenges

The Chinese Ministry of Health has made diabetes prevention and treatment a priority. In October 2010, it announced the launch of a 3-year program to train 100,000 community-level doctors across China in both pediatric and adult diabetes prevention and treatment. Unfortunately, international assessment of these programs has shown them to be insufficient, limited in scope, and not regularly updated.

There is also the challenge of patient education, which is key to any preventative measure. Wang et al. (2017) suggest that approximately 50% of the Chinese population may be pre-diabetic and just over 30% of those with either type 1 or type 2 diabetes remain unaware of their condition. Regional treatment and disease recognition are equally challenging. Recently, the Chinese Diabetes Education Status Survey Group surveyed some 6000 type 2 diabetic patients in 50 regional medical centers and found that only 32.1% had reached acceptable HbA1c levels (<7%) despite the purported availability of treatment. This was compared to data from the US, which showed that 50.1% had HbA1c levels below 7% [6]. A 2012 study by Li Ming-Zi et al. provided data from 6 tertiary Beijing hospitals, which surveyed 1,151 type 2 diabetics and found only 37.8% were maintaining their HbA1c levels below 7% [7].

Another layer of complexity arises from the fact that the overall problem is complicated by genetics. As a general rule, East Asian peoples have repeatedly been shown to have considerably less physiological tolerance to excess adipose tissue than other populations. Ten extra pounds on a pre-teen Asian child is considerably more dangerous than the same excess weight on a European or African child of equivalent age and build.  

There is also considerable ethnic diversity in China. Although the Han Chinese constitute the majority of the country’s population (~91%), they are markedly in the minority in areas populated by other ethnic groups, such as the Hui, Tibetan, and Uyghur peoples. Through a combination of genetic factors and lifestyle, the incidence of diabetes in non-Han populations tends to be significantly less. For example, Tibetan peoples had the lowest prevalence of diabetes and prediabetes (4.3% and 31.3%, respectively) in the country [8]. Accordingly, anti-diabetes programs need to be keyed to the ethnic composition of the regions where they are implemented.  

Finally, an important factor not generally recognized in the West is the persistent role of traditional Chinese herbalism in treating many disorders, including diabetes. This past January, there was a remarkable flurry of interest in Tianqi, a mixture of 10 herbs that was touted as effective in preventing pre-diabetics from developing clinical disease. 

Overall, the key challenges remain: 1) A dearth of knowledge about and implementation of prophylaxis to stem the tide of juvenile obesity, 2) Low and late diagnosis and treatment of diabetic patients, 3) Poor glycemic control and compliance among diagnosed patients, 4) The absence of effective recognition and diagnosis of the disease in a huge pre-diabetes population, and 5) The coming increase in diabetes-related complications.

Approaches to Intervention

Popular publications, as well as medical journals, almost unanimously stress the exceptional need for supplemental medical education for both physicians and the lay population. Research undertaken by the BMS Foundation cites a continuing shortage of well-trained healthcare professionals with training in either juvenile or adult diabetes. Moreover, as recently as 2016, physicians were found to spend less than 6 hours with each diagnosed diabetes patient per year. Accordingly, increasing awareness of and access to appropriate diabetes treatment will not be sufficient. 

Attempts at interventional success will also be complicated by the lack of the millions of trained nursing home employees needed to care for a rapidly growing elderly population, which in China, as elsewhere will be more heavily impacted by diabetes and its complications. 

In addition, perhaps the most glaring problem in achieving an effective plan for diabetes prevention and treatment in China is the imbalance of resources in the healthcare system, which nationwide, favors urban over rural populations.

While the government of China is introducing reforms and education to tackle the diabetes epidemic, there are certain legislative barriers that handicap the effectiveness of proposed solutions. For example, the direct advertising of prescription drugs is prohibited. Thus, both patients and healthcare professionals are obliged to look elsewhere for information, a situation which favors any media that provides information.  

Despite the huge number of social media users in China, this route has been relatively underexploited.  In recognition of this, a consortium of healthcare professionals from Johns Hopkins Medical School, the International Medical Crisis Response Alliance (IMCRA), and the DaiAi Shenzhen Diabetes Initiative (DSDI) has undertaken the development of a series of online patient/physician forums. This program, launched in 2016 at 2nd People’s Hospital in Shenzhen, China uses social media-accessed targeted streaming video modules to satisfy the largely unmet need for education, support, and interactivity with Chinese-speaking medical experts. The effort has focused largely on diabetes prevention and recognition of the threat of obesity and worsening nutritional practices effecting Chinese children. While this type of media approach has been highly effective in pilot urban centers, such as Shenzhen and Guangzhou, its critically important impact in establishing a platform for reaching rural communities is increasing. As recognized in a recent report by Sanofi, access to programs like the IMCRA-DSDI initiative through use of a digital information portals accessible through smartphones may drive a breakthrough. To quote Sanofi directly: “…the use of apps which allow for remote management and consultation…is an important area of development.” 

As of November 2018, the essential IMCRA-DSDI-Johns Hopkins program has utilized the following approach in designing dual HCP and patient-targeted systems:
  • Selection of well-published and well-known Chinese-fluent faculty and advisors who would be seen as KOLs by healthcare professionals and the targeted populations
  • Establish a focus on why diabetes has become epidemic in East Asian countries like China, Indonesia, and Vietnam (e.g., Westernization of diet and physical activity levels)
  • Query those faculty and advisors who have experience in China as to practice gaps, target locales and most serious problems as they see them. 
The video modules recorded to date have focused on the following topics:
  • Why has diabetes become epidemic in China and certain other East Asian nations?
  • Diabetes risks and prevention in childhood
  • Impact of pathological HbA1c levels on economic health and the general well-being of population
  • Prevention: lifestyle and eating habits
  • Managing the pre-diabetic patient
  • Initial approach and treatment options for type 2 diabetes
  • Managing comorbidities
  • When to use insulin and other drugs
  • Cultural and psychosocial issues in endocrinology practice
  • Utilizing the interactivity components of the IMCRA-DaiAI system
System initiation outcomes have been assessed using the following parameters:
  • Metrics on system use and uptake
  • Metrics on interactivity and response to monitoring questions
  • Personal assessments of system impact on users practice
  • Before/After evaluations of program effectiveness by virtue of patient outcomes and impact on practice, clinic and hospital standing
  • Assessment of program expansion appropriate to need
IMCRA-DSDI-Johns Hopkins Program Links

References
 
[1] Yang W-Y. Achieve Great Success, and Blaze Trail: Review of Clinical and Basic Research Progress of Chinese Diabetes in the 21st Century. Chinese Medical Journal 2009; 122(21):2525-2529.
[2] Yang W-Y, et al. Prevalence of Diabetes among Men and Women in China. N Engl J Med 2010; 362(12):1090-101
[3] Wang L, et al. Prevalence and Ethnic Pattern of Diabetes and Prediabetes in China. JAMA 2017; 317(24):2515-2523.
[4] International Diabetes Federation (2010). New Diabetes Figures in China: IDF Press Statement.
[5] International Diabetes Federation (2010). China Spends RMB 173.4 Billion (US $25 Billion) a Year on Diabetes Treatment.
[6] Chinese Diabetes Education Status Survey Group. A Nationwide Survey of Diabetes Education, Self-management and Glycemic Control in Patients with Type 2 Diabetes in China. Chinese Medical Journal 2012; 125(23):4175-4180.
[7] Li M-Z. Management Status of Type 2 Diabetes Mellitus in Tertiary Hospitals in Beijing: Gap between Guideline and Reality. Chinese Medical Journal 2012; 125(23):4185-4189.
[8] Hu C and Jia W. Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication. Diabetes 2018; 67:3-11.

Written by Thomas G. Hedberg, PhD, Clinical Development
 
 
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Strategies to CMC New Product Development and Manufacturing Outsourcing Resources (cont.)


Business Model and Corporate Strategy
 
The first step is to understand the company’s overall business model. Does the company plan to fully develop and gain NDA approval, then manufacture, commercialize, and market its own product? Alternatively, does the company plan to develop a prototype product ready for a first-in-man evaluation, maybe gain a patent, and then license the prototype and patent to an organization that will gain NDA approval and eventual commercial success? Or does the company’s plan fall in between these two ends of the spectrum? 
 
The company’s larger corporate strategy also affects strategic outsourcing. Corporate strategies drive a buy versus build decision when it comes to outsourcing a company’s new product development and/or commercial manufacturing capability. Examples of corporate strategies include: “Bring a large number of products to market with lower risk by being in multiple therapeutic areas” or “Develop and commercialize new pharmaceutical products with greater efficacy and safety profiles by employing advanced drug delivery techniques and technologies.” 
 
Selecting a CRO or CDMO

The second step in developing a CMC and outsourcing strategy is to consider different CROs (Contract Research Organization) or CDMOs (Contract Development and Manufacturing Organization) that the company will work with. CROs and CDMOs come in various shapes and sizes. Some are large and have the full breadth of capabilities, from basic research and development to commercial scale production, packaging, and distribution. Other organizations, including university laboratories open to funding and collaborations, are smaller and perhaps niche capability providers.
 
If the company’s business model is to only develop a prototype, get a patent, and then license the product, either a niche CRO or full service CDMO will meet its needs. Smaller CROs will typically be less expensive and quicker in delivering CDAs (Confidentially Agreement), timelines, costs, deliverables, and proposals, allowing for a more rapid project progression. On the other hand, a large CDMO will be more rigid and slower in putting the necessary business agreements in place. Despite what full services CDMOs promise to smaller companies, the small company’s project will be a lower priority in the CDMO’s overall customer portfolio. A Development Agreement with the smaller company will only bring a short-term finite source of revenue for the CDMO in contrast to a long-term Commercial Supply Agreement with a company committed to full development and commercial production.
 
If the company’s business model is to develop and commercialize its products, then either a small CRO or full service CDMO will suffice. The small CRO may allow the company to move through an early screening/feasibility assessment more rapidly than a large CDMO. However, the small CRO would need to perform a Technology Transfer to the full service CDMO for full development under GMP conditions in order to supply CTMs (Clinical Trial Materials) and eventual scale-up, validation, and analytical method development required for later stage development and eventual commercialization. This need for Technology Transfer usually results in high costs and loss of time to market.
 
If the company’s business model is to develop pharmaceutical products in multiple therapeutic areas employing different physical product presentations and formulations (e.g., tablets, creams/ointments, solutions, oral sprays) for a wide but shallow product line, the company may consider CROs/CDMOs. However, the company should wait until its development pipeline and commercialized product line mature and increase with certainty as part of the “buy versus build” decision.
 
If the company’s business model is to improve the safety and efficacy profiles of a pharmacologically active substance through proprietary drug delivery technologies, then a small or large CRO/CDMO or university laboratory would work. Although a Technology Transfer would be involved, proprietary enabling technology can provide advantages to offset that consideration. What should be kept in mind, however, when working with university labs is the university lab’s “research mindset” and approach, which typically means little experience in pragmatic product development that leads to regulatory approval and commercialization. Additionally, universities usually charge generous overhead fees on top of the laboratory’s proposal and cost of working with the lab. Most universities are also aggressive in leveraging university-owned intellectual property in terms of licensing fees and downstream royalties on commercial sales revenue.
 
With all these things in mind, a few other considerations in selecting the most appropriate and capable CRO/CDMO for your project are: (1) business processes, (2) facility, and (3) people. 
 
Business Processes

Business processes include: getting the CDA in place; receiving the initial early development and feasibility timing and cost proposal; and obtaining amendments, downstream licensing, and commercialization agreements. The timing of CDA and development proposal turnaround is a clear indicator of how the later stage proposals and project progression will proceed.
 
Additional questions to consider are: 
  • How will the project be managed? Does the CRO/CDMO have a formal project management system and named individual? 
  • Does the organization have an explicit and well-documented quality policy, including process performance metrics and systems governing SOPs (Standard Operating Procedure)? 
  • Will you communicate with the laboratory person performing the function or an intermediary that relays information back and forth to you as a client? 
  • What is the frequency of in-person meetings and phone calls for project status updates?
  • Will meeting minutes documenting project progression and key dates for deliverables be issued? 
  • Is there an established escalation process for dispute resolution due to changes in scope, changes in timeline, invoicing, and unfavorable results? 
Overall, you should expect all business processes to be client-friendly, quick, and flexible to meet the needs of your company to the greatest extent possible.
 
Facility

The physical plant and laboratories are important, especially if the data generated is intended to be used as part of any regulatory submission. You would always want to know that your product is well-cared for. Questions to consider in evaluating the facility are: 
  • Does the facility support CGMP (Current Good Manufacturing Practice) compliance? Can it pass a preapproval regulatory inspection? 
  • Is the water system up-to-date and tested regularly?
  • Is the air handling system installed and documented properly to assure no cross-contamination between your product and another company’s product? 
  • When was the last regulatory inspection? Were there any observations issued by the inspector? Ask for a copy of the EIR (Establishment Inspection Report).

People

One of the most important aspects in working with a third-party provider is active involvement with the CRO/CDMO. Do not think that once the contract is signed that you can expect deliverables 6 months later without any communication, input, and decision-making on your end. The provider will need your direction and decision on many matters along the way. You should be timely and responsive to the CRO/CDMO’s questions. Additionally, you should consider your service provider’s suggestions on approaches, associated risks, and timings. Although the service provider has confidentiality obligations, it can still make suggestions based on learnings from previous projects, which could lower costs and increase efficiency for your company.
 
You should also identify an internal advocate for your project within the CRO/CDMO. This does not necessarily need to be the most senior individual, but should be someone who connects with you, your project, and your company’s mission; oftentimes this person will have a personal reason for their particular interest in your program. This internal advocate can be effective in moving along the contractor’s resource allocations and priorities. 
 
Conclusion

There are several issues to consider when identifying and working with the appropriate CRO/CDMO, some of which are hard issues and others soft. Kinexum is uniquely positioned in its ability to assemble a multi-functional team of experienced industry and regulatory experts to provide both strategic advice and tactical operational oversight and management to move your project from a good idea through regulatory approval and eventual successful commercialization.

Written by David H. Bergstrom, PhD, Kinexum CMC Consultant
 
 
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Applications of Machine Learning in Healthcare: Scalable Treatment Solutions for Cancer Patients (cont.) 

 
There are many barriers to democratize access to cancer care. Finding the right diagnosis, the best doctors, and the most applicable treatment is already a challenging task for patients with access to insurance and healthcare resources in developed countries. In developing countries, such as India, each of these tasks becomes exponentially more difficult. In 2014, it was estimated that there was only one oncologist per 16,000 cancer patients in India. In stark contrast, the American Society of Clinical Oncology (ASCO) predicts that the United States will have one oncologist per 100 patients by 2020 [1]. 
 
Navya Network is one company that offers a service to reduce this disparity. Navya applies an innovative, machine learning-based informatics system to provide scalable treatment solutions to patients already diagnosed with cancer. Distinct from telemedicine solutions, which connect patients to doctors directly, Navya’s technology structures patients’ medical reports and preferences into an ontology. An ontology, in essence, is a standardized way of organizing the definitions and relationships between concepts. With patients’ data in a consistent format, Navya matches it with clinical trials and previous patients’ experience to create a case summary with an ordered list of treatment recommendations. Because of how tightly structured this summary is, an oncologist can review it and offer feedback in a matter of minutes. Navya sends this summary to multiple oncologists and aggregates their opinions into a treatment plan, which is written in language accessible to patients.
 
Clinical validation trials confirmed that treatment recommendations from Navya’s clinical informatics system are 98% concordant with predictions of tumor boards and expert panels at Tata Memorial Centre in India and Olive View-UCLA Medical Center in California [2]. The company has assisted over 26,000 patients. Although these patients are predominantly from India, the company is broadening its scope and has reached patients in 68 countries. 
 
This article will first describe the gap analysis that led to the creation of the Navya approach, while highlighting how the company’s solution is distinct from telemedicine. It will then give an overview of how Navya has used machine learning to create a scalable approach to democratizing cancer treatment decisions.
 
Filling a Treatment Decision Support Gap for Doctors and Patients

The motivations of Navya’s founders, Ms. Gitika Srivastava and Dr. Naresh Ramarajan, were both personal and professional. The idea arose from the pair’s experience with cancer care and was refined with a careful gap analysis.
 
In 2007, each learned their close relatives had been diagnosed with cancer. Ms. Srivastava remembers how her family found the process of finding the right treatment agonizing, not least because of difficulties in finding reliable, accurate information. Despite a strong academic and technical background, she “had no way to know where to find papers [in academic journals], how to read them, and how to interpret their statistics, analysis, applicability, and complex words.” Ms. Srivastava emphasizes that the first goal of Navya was to provide an empathetic, personal service that would provide the clarity needed to simplify treatment decisions and the complex medical literature surrounding them.
 
As a resident at a tertiary care center, Dr. Ramarajan realized that finding reliable information was not just challenging for patients, but for doctors as well. He saw that one of the biggest barriers to scaling cancer treatment was the amount of time doctors had to spend sorting through data: synthesizing a patient’s case, staying abreast of the latest medical literature, and determining which treatments from literature apply to one instance of cancer. After processing the necessary data, the actual decision-making time of doctors is typically only a few minutes or seconds per case. The founders realized that if they could remove cancer experts’ burden of drudging through data and use them only for their decision making ability, they could give experts the means to reach an order of magnitude more patients. 
 
A critical decision for Navya was to focus exclusively on treatment decisions, not diagnosis. First, no one else was doing it. Dr. Ramarajan emphasizes that once a patient is diagnosed, that is when they have a complex tradeoff to make. At the time, no service approached treatment decisions that could simultaneously keep patient preferences in mind, take multiple expert opinions into account, and exhaustively factor in clinical evidence.
 
Navya entered a landscape where existing solutions, either for diagnosis or treatment, tried to replace a visit to a doctor, rather than taking advantage of specialists’ unique expertise and ability to collaborate. Telemedicine solutions, where an expert reviews a patient’s case files and either responds with a writeup or a short phone call, is one popular alternative to an in-person visit. However, patients are still left with the task of consulting multiple experts and synthesizing their opinions. The company recognized that, short of having access to a tumor board in a tertiary care center, patients did not have adequate resources to connect them to experts and help them come to a consensus opinion. 
 
The second reason for focusing on treatment decisions was that diagnostic decision making is a far harder machine learning problem to crack. Diagnostic decision making is backed by heuristics, rules, and clinical practice based on incomplete information. Because treatment decisions could use a more data-driven model, Navya would be able to more easily evaluate whether it was producing correct results. 
 
A third advantage was that Navya could work directly with patients and remain an analytics software company. Because patients would have already undergone the necessary tests to receive a diagnosis, Navya could directly help patients with their next need: making treatment decisions. In contrast, working with doctors on diagnoses would have inserted the company into the practice of medicine. This would force Navya to provide decisions based on incomplete information, would necessitate that Navya frequently advise patients to get further tests, and could have exposed the company to further regulation as a diagnostic service.
 
Similarly, Navya’s decision to focus on India made sense from both personal and business perspectives. Ms. Srivastava was motivated by a personal desire to give back to her country, especially to Tata Memorial Centre, the tertiary care center that had treated her family member. Ms. Srivastava knew that the hospital’s oncologists were “extremely evidence-based, research-based, and collaborative.” As one of the largest tertiary care centers in Asia, it also had the benefit of experts from many different specialties under one umbrella. Tata Memorial Centre agreed to commit its entire staff of oncologists as members of Navya’s Expert Panel in exchange for co-branding of Navya’s Expert Opinion Service. 
 
India also proved to be an ideal setting to develop and test Navya’s concepts, as it provided an environment that was by turns complex, chaotic, and collaborative. India’s system of cancer care also had some of the largest disparities in the world. In 2014, India had only 0.6 physicians and 0.9 beds per 1000 people, over three times less than in the United States [1]. India’s disparity in quality of care, especially between rural and urban areas, could benefit greatly from an online service. In fact, patients in a prospective study incurred 40% of their care expenses before setting foot in a hospital [3]. 
 
Navya had decided its goal was to provide a service that would use technology to empower patients with treatment decisions, while minimizing the time physicians spent synthesizing information. India offered Navya the chance to partner with one of the world’s largest tertiary care centers, an ideal test environment, and a chance to make a real impact on patients’ treatment decisions.
 
Using Machine Learning to Make Treatment Decisions By Structuring Evidence and Experience into an Ontology

Navya’s machine learning solutions are designed to answer the question, “What is the best treatment for a patient’s case?” The process works as follows: patients upload their medical reports to Navya’s portal. After a clinically-trained analyst verifies that all necessary reports are in place, the case is run through Navya’s clinical informatics system, which returns a list of treatments ranked by applicability to the patient. This list is sent to organ-specific specialists relevant to the patient’s case, who tweak the recommendations as needed. Once all of the recommendations are finalized, Navya uses more machine learning to weigh and combine the opinions of all the experts who reviewed the case before synthesizing them into a report. The analyst verifies the report, tweaks the language to be as accessible as possible, and returns it to the patient. This process takes a median time of 24 hours [4].
 
When processing a patient’s case, Navya uses machine learning in ways that can be grouped into “evidence” and “experience” based engines for making decisions. 
 
The evidence engine serves two purposes. First, it synthesizes existing medical literature. For many cases, a number of different trials apply, but their results conflict. Navya uses machine learning metrics to rank the strength and the quality of these trials. Second, the evidence engine ranks the applicability of clinical trials to a single patient. Navya calls this metric the “applicability index.” For example, a clinical trial might take all comers over 18 years of age, but may not have enrolled anybody who is over 70 years of age, and so would be less applicable to elderly patients. From the results of this process, Navya can determine how well-represented one patient is in a trial or population of trials. Based on these results, the clinical informatics system can determine how likely it is that the results of a trial would be applicable to a specific patient.
 
While the evidence engine provides a solution for doctors who do not have time to sort through new clinical trials, the experience engine gives Navya the tools to support patients whose unique and complex cases are not described adequately in literature. In fact, a large number of patients’ diagnoses do not match any clinical trials. The evidence engine uses the data of past patients at Tata Memorial Centre, including past patients’ outcomes, treatment pathways, and decisions. Using this information, the engine can model a patient decision based on the current patient’s similarity to a cluster of patients that have already gone through similar treatments. One of the largest advantages of the experience engine is that it adapts its model using oncologists’ feedback. After a list of treatments is presented to an oncologist, the oncologist can tweak the system’s recommendations before sending it to the patient. This feedback is fed back into experience engine, which allows it to refine its model. This raises the confidence of future predictions and increases the breadth of cancers it can support over time.
 
Navya, then, can efficiently find all of the clinical trials that are relevant to a patient’s case and rank them by quality and applicability; it can also suggest treatments based off of previous patient outcomes. That said, what allows Navya’s clinical informatics system to achieve such a high degree of concordance with expert oncologists in real world settings? The answer is how Navya stores its data. Every piece of patient medical information, every treatment decision and outcome, and every clinical report is restructured into a curated ontology. The ontology is more than a list of terms and definitions; it describes terms’ synonyms, contexts, and relationships with other terms. The most powerful effect of this ontology is that it allows Navya to replicate the intuition that oncologists pick up after years of working on patient cases. Dr. Ramarajan describes that the ontology allows the informatics system to relate things that seem distinct, like a side effect from a treatment to a patient’s pre-existing medical condition. These relationships allow the system to make inferences, strengthening its predictions. As a result, Navya’s expert and experience engines can recommend treatments that replicate the conclusions of an experienced oncologist who is well versed in the patient’s medical history and the applicable literature. 
 
Conclusion

Machine learning provides an innovative and readily scalable solution in the landscape of democratizing cancer care, one that can be uniquely tailored to patients facing treatment decisions for complex cancers. By eliminating the time it takes doctors to review the details of a patient's case, and allowing multiple doctors to collaborate asynchronously on a case, Navya escapes the one-doctor one-patient model that limits telemedicine solutions. Because of Navya’s use of a structured ontology, Navya’s machine learning solutions offer the ability to replicate a tumor board’s intuition using machine learning, providing support for patients whose cases are not covered adequately in literature, with 98% concordance to oncologist recommendations at Tata Memorial Centre and Olive View-UCLA Medical Center. Especially in areas where the geographic and monetary barriers of accessing a tumor board at a tertiary care center prevent patients from finding the best treatment option, Navya offers a solution that is truly scalable.
 
References

1. Goss PE, et al. Challenges to effective cancer control in China, India, and Russia. Lancet Oncol 2014; 15(5):489-538.
2. Ramarajan N, et al. International application of an online clinical informatics expert system for breast cancer. Journal of Clinical Oncology 2017; 34:15_suppl, e18037-e18037.
3. Mohanti BK, et al. Estimating the Economic Burden of Cancer at a Tertiary Public Hospital: A Study at the All India Institute of Medical Sciences. [discussion paper]. New Delhi: Indian Statistical Institute, Delhi Planning Unit; 2011. 
4. Badwe RA, et al. Global impact of a clinical informatics system: Scalable delivery of on-time access to evidence-based multidisciplinary expert treatment decision systems for all cancers. Journal of Clinical Oncology 2017; 35:15_suppl, 6502-6502.

Written by Michael Cobb, Navya Network Software Engineer
 
 
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Kinexum Webcast Recap: Using Metformin to Target Aging (cont.)

 
Kinexum Founder and Executive Chairman Zan Fleming led the medical review at FDA, which resulted in metformin’s approval in 1995, which was controversial at the time. In addition, Kinexum is part of the Metabesity movement aimed at extending healthspan­—the amount of life spent healthy rather than debilitated by chronic diseases of aging (such as diabetes, cardiovascular and neurodegenerative diseases, and some cancers)—by seeking to identify and modify common underlying metabolic roots of such diseases.
 
The TAME trial represents a pioneering engagement with FDA on how clinical trials that target aging may be approached. Although metformin is an old generic drug that may not generate much more incremental revenue for a sponsor, it represents only the first generation of compounds to target aging. The TAME trial will gather valuable information on parameters that could guide the development of aging-related biomarkers that may yield more robust drugs.

 
BACKGROUND 
 
The Geroscience Hypothesis 

The “geroscience hypothesis” posits that the manipulation of aging will delay (in parallel) the appearance or severity of many chronic diseases because these diseases share the same underlying major risk factor—age [1]. 
 
The resulting model for developing geroscience interventions differs from the prevalent model of the development, regulatory approval, and clinical application of drugs and other interventions. In the conventional model, drugs and biological agents are targeted to treat or prevent a single disease. This approach does not accommodate a single indication that consists of multiple diseases. Instead, the conventional model permits a single product to be developed for multiple disease indications, but only on the basis of evidence for safety and efficacy from studies focused on each disease. 
 
Underlying the geroscience hypothesis is the expectation that age is more than just a risk factor, but rather, a biological condition that can be mechanistically approached to reduce the risk or occurrence of multiple age-related conditions. The geroscience hypothesis is based only in part on the strong association of chronic conditions, including cancer, dementia, and cardiovascular disease, with age, and the exponential increase in rates of these conditions with advancing age [2]. The number of annual deaths from these diseases also increases logarithmically with age [3]. Just as important to the geroscience hypothesis is the growing understanding that metabolic, inflammatory, and other effectors mediate the aging process and that these effectors can be modified. Even now, age-related conditions are separately managed and, in some cases, prevented; geroscience offers the prospects for collectively targeting these conditions. The result could be an increase in the average span of years free from major diseases—that is, an increase in healthspan.
 
Extending Lifespan and Healthspan in Animal Models

The National Institute for Aging (NIA)’s Intervention Testing Program (ITP) has confirmed the geroscience hypothesis in mouse models. The Intervention Testing Program is a multi-institutional resource for investigating treatments, ranging from pharmaceuticals and hormones to dietary supplements, which have the potential to extend lifespan and delay disease and dysfunction in mice. Of the 16 compounds studied so far, 5 have yielded positive results: NDGA (nordihydroguiareticacid), aspirin, rapamycin, acarbose, and 17-a estradiol [4-6]. Rapamycin extends life span in both male and female mice, while the other four compounds have demonstrated larger effects in males than in females.
 
DESIGNING A CLINICAL TRIAL TARGETING AGING
 
Testing the Geroscience Hypothesis in Humans

Prevention studies can be performed to apply the geroscience hypothesis to humans. However, these studies require a candidate that is not only effective, but particularly safe, and ideally, inexpensive, since they need to follow subjects over some years to demonstrate disease prevention or delay. 
 
The figure below shows the evaluation continuum of clinical research that can be performed in humans. From changes in cellular gene expression to extending lifespan, the research increases not only in relevancy to human health, but also in time and expenses. 

Evaluation Continuum

At this early stage of aging research, FDA’s interest along the continuum starts only with the prevention of age-related disease, as opposed to aging generally. However, the Agency noted it would consider new indications relevant to aging research if the TAME researchers could establish the prevention or delay of disease. 
 
What is the efficacy outcome for a clinical trial targeting aging biology? 

The answer is not a single disease-related endpoint, but a composite endpoint of multiple-age related diseases. The outcome of the TAME trial is the time until the occurrence of one of a collection of possible diseases. These diseases share few factors apart from their association with increased age. FDA advised that biochemical-based diagnoses and conditions based on physiological measurements, such as diabetes and hyperlipidemia, would not provide convincing evidence; rather, only substantial clinical outcomes should be used. The diseases selected for the composite outcome are: myocardial infarction (MI), stroke, congestive heart failure (CHF), cancer (excluding prostate and non-melanoma skin cancer), mild cognitive impairment (MCI), dementia, and death.
 
Although different endpoints may appeal to different audiences, all the various endpoints need to first be linked together. Results from the TAME trial could help to promote FDA acceptance and pave the way for future trials of compounds that, on the basis of biomarkers and intermediate outcomes, show promise for improving composite clinical endpoints. 
 
Treatment of Choice: Metformin

The TAME investigators will use metformin as the treatment. Discovered in the 1920s, metformin is a biguanide class drug and is the most widely prescribed antidiabetic drug in the world. Metformin addresses diabetes by decreasing hepatic gluconeogenesis, but the drug can also perform other functions. 
 
What makes metformin attractive for this trial, even though the drug has been around for several decades? There are three major reasons: 
  1. Metformin modulates key pathways in the biology of aging and has been shown in animal models to target aging to delay or prevent disease;
  2. There exists preliminary evidence that it reduces the onset of disparate diseases in humans; 
  3. Most importantly from a prevention perspective, it has been used safely for over 60 years. 
 
Metformin has additional perks for consideration of its use in a clinical trial: it is inexpensive and available as a generic drug. 
 
What evidence is currently available? 

Mechanism of action
Metformin targets multiple pathways on aging and leads to:
  • decreased insulin levels;
  • decreased insulin/insulin-like growth factor (IGF)-1 signaling;
  • inhibition of mechanistic target of rapamycin (mTOR);
  • inhibition of mitochondrial complex 1 in the electron transport chain;
  • reduction of endogenous production of reactive oxygen species (ROS);
  • activation of AMP-activated kinase (AMPK);
  • and reduction in DNA damage [7]. 
Animal models
Data from experiments introducing metformin to mice at midlife showed a 6% extension in lifespan. This extension in lifespan came with a 25% healthspan benefit, in which body weight was preserved, energy homeostasis was shifted to increase the use of lipids, and general fitness and physical performance were improved [8].
 
Human Observational Studies
Observational studies of metformin and CVD outcomes show approximately a 20% reduction in CVD outcomes [9-10].
 
A meta-analysis of data that compared people with diabetes taking metformin to other diabetic medications showed a 31% decrease in cancer incidence and a 33% decrease in cancer mortality in the metformin group [11].
 
Another meta-analysis showed a 45% reduction in cognitive impairment and a 24% reduction in dementia in people using metformin [12].
 
Additional observational studies from Campbell et al. and the UKPDS study, one of the first large trials of metformin in people with early onset diabetes, showed a substantial reduction in total mortality; the hazard ratio was a 25% reduction in death from any disease [13-14].
 
To summarize, metformin meets the criteria for an agent in an age-related endpoint trial: (1) it’s safe; (2) it affects the pathways of aging biology; and (3) epidemiologic and some controlled trial data link metformin to reduced rates of diseases as disparate as cancer and dementia.
 
TAME TRIAL DESIGN
 
The TAME trial is a five-year, double-blind, randomized, placebo-controlled trial. Because up to 25% of people who take metformin can develop severe gastrointestinal side effects [15], the researchers have included an open-label run-in. The selected dose is 1500 mg slow-release metformin taken once per day. The median follow-up time will be 45 months. 
 
This trial is expected to enroll 3000 patients, which is no larger than most other prevention trials. The inclusion criteria are: 65-80 years of age with a gait speed 0.4-1 m/s and/or age-related disease (e.g., CVD, cancer, MCI). Slower gait speed is a primary entry criterion because it is a gerontologic sign of increased risk for age-related disease. If a patient has an age-related disease at the time of enrollment, the same disease does not qualify as an endpoint for that patient. The exclusion criteria are: people with diabetes, impaired kidney function, dementia, and/or life-threatening disease so far advanced that an intervention on age-related pathways would not have much effect on their trajectory of prognosis. 
 
The primary outcome is clinical: the time to incidence of any new major age-related disease. These diseases include MI, stroke, CHF, cancer (excluding prostate and non-melanoma skin cancer), MCI/dementia, or death. FDA is most interested in these hard clinical outcomes to support consideration of new indications for metformin.
 
The secondary outcome is functional: the time to incidence of disability, which includes major decline in mobility, cognitive function, or activities of daily life (ADL). Supporting analyses will involve changes in patient-centered outcomes and continuous measures of physical and cognitive performance. 
 
The tertiary outcomes are biological: changes in metformin levels and biomarkers of aging and age-related diseases. These outcomes are to provide convergent evidence of broad age-related effects, though they are currently not FDA-validated biomarkers.

Food for Thought

Dr. Barzilai concluded the presentation with four comments: 
  1. The maximum human lifespan potential appears to be around 115 years, based on reported world-record survivors. However, humans live, on average, up to the age of 80. Thus, several decades of lifespan can potentially be captured by effective interventions.
  2. Though overall economic and treatment costs of age-related diseases increase with age, these costs will be offset by the reduction in healthcare costs and increase in productivity. The healthspan dividend is compelling: the economic value of delayed aging and the associated onset of cardiovascular disease, cancer, diabetes, obesity, arthritis, and Alzheimer’s disease is estimated to be $7.1 trillion over fifty years. Metformin may turn out to be a weak drug to target aging, but It will pave the way for other drugs to be developed.
  3. Biological aging is not the same as chronological aging. Aging does not solely describe people who are getting older. People with HIV or cancer survivors who undergo radiation therapy are biologically years older than their chronological age. The need to find drugs that affect aging affects more than just the elderly and is critical to address.  
  4. A barrier to promoting progress in pharmacologic interventions directed at multiple age-related conditions is the lack of an established indication at FDA. Without this indication, healthcare providers will not pay for drugs. Without payers, pharmaceutical companies will not be incented to develop drugs. The TAME trial is leading the way for the potential of an aging indication at FDA. This, in turn, could lead to 80, 100, or even 120 years of age being the new (hale) 60. 
 
Additional Resources

The full recording of the webinar can be found here.
 
The TAME executive committee consists of Nir Barzilai, Vanita Aroda, Mark Espeland, Jamie Justice, George Kuchel, and Stephen Kritchevsky. Dr. Kritchevsky can be reached at  This email address is being protected from spambots. You need JavaScript enabled to view it. , and Dr. Barzilai can be reached at  This email address is being protected from spambots. You need JavaScript enabled to view it. .

References

[1] Sierra F, Kohanski R. Geroscience 2017;39(1):1-5.
[2] St Sauver JL, et al. BMJ Open 2015; 5:e006413; Goodman RA, et al, Prev Chronic Dis 2013;10:E66.
[3] Millbank Quarterly. 80(1). 2002 (from 1997 U.S. Vital Statistics).
[4] Strong R, et al. Aging Cell 2016;15:872-884.
[5] Harrison DE, et al. Aging Cell 2014;13:273-282.
[6] Strong R, et al. Aging Cell 2008;7:641-650.
[7] Barzilai N, et al. Cell Metab 2016;23(6):1060-1065.
[8] Martin-Montalvo A, et al. Nat Commun 2013;4:2192.
[9] Roumie CL, et al. Ann Intern Med 2012;157(9):601-610.
[10] Aguilar D, et al. Circ Heart Fail 2011;4(1):53-8.
[11] Gandini S, et al. Cancer Prev Res 2014;7:867-85.
[12] Campbell JM, et al. J Alzh Dis 2018;65: 1225-38.
[13] Campbell JM, et al. Aging Res Rev (2017) 30:31-44.
[14] UKPDS NEJM 2008; 359:1577.
[15] McCreight LJ, et al. Diabetologia 2016;59(3):426-35.

Written by Jennifer Zhao, Kinexum Associate
 
 
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News and Upcoming Events

 
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News

 

Monica Lynn Schaich - Forever In Our Hearts

A colleague is defined as a person with whom one works. An expert is defined as a person has special skill in a particular area.  A friend is defined as a person who you are fond of, talk with, or spend time with.

Monica Lynn Schaich was all of these and more to our Kinexum family. Since joining Kinexum in 2007, Lynn provided our clients unconditional support with professionalism and integrity. Zan Fleming, MD, Founder and Executive Chairman of Kinexum, speaks fondly, “Lynn was the best in the business when it came to QA and QC.”

Lynn Schaich was the voice on the phone you knew to be truthful and honest. However, she was much more than a voice; Lynn was also a loving daughter and wife. She was a true friend who could always be counted on. As expressed by Dr. John Whisnant: “Lynn was our example, in working and in staying focused for many years. Our thoughts and prayers are with her family and friends.”

Monica Lynn Schaich lived 1955–2018, but it is neither the beginning nor the end that is most important. It is the “–“, the in-between, that will be remembered. Lynn lived her life to the fullest with compassion, laughter, and humility. This is what we will remember and hold close in our hearts forever.

To view Lynn’s obituary, please click here.

 
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New Kinexum Team Member(s)

 

Brian Oscherwitz
Brian Oscherwitz, MBA, PMP
Clinical Operations/Project Management 
Learn more about Brian
 
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Upcoming Webcasts

 

PTSD–Associated Pathologies in the World of 2018
Join Kinexum's upcoming webcast with Thomas Hedberg, PhD, executive director of the United Nations-affiliated NGO, International Medical Crisis Response Alliance (IMCRA), on the causes, effects, and global aspects of post-traumatic stress disorder (PTSD) and responses to current treatment failure. Register here
 
The Rock Just Below the Water: Market Access Risk and Early-stage Biotechs
Join Ed Saltzman, Cello Health BioConsulting, (previously Defined Health), Roger Longman and Jeffrey Berkowitz, Real Endpoints, and Dennis Purcell, Aisling Capital for a collegial, in-depth discussion on the necessity of early-stage biotech to consider payer-relevant clinical development plans. Register here
 
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Upcoming Conferences

 

Kinexum executives and leading experts will attend the following conferences. If you are interested in meeting with a Kinexum representative at these conferences, please contact  This email address is being protected from spambots. You need JavaScript enabled to view it. .
JP Morgan Healthcare Conference 2019
ENDO Conference 2019
 
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