Tag: Biotech

Series A-2: What diseases are they working on?

UBS summarized in its China’s biotech report – “Oncology: A driving force for innovation in Chinese biotech”.

An article by Pharmacodia in 2017 said the top 3 therapeutic areas for biologics in China are cancer, rheumatoid arthritis (RA), hepatitis B virus (HBV)[1].

Another bluebook in 2017 outlined the five focus areas for China’s biotechnologies[2]: 1) vaccines, 2) mAb and protein drugs for cancer, cardiovascular, neurodegenerative, diabetes, autoimmune diseases, 3) diagnostics and screening for major diseases, 4) gene therapies, cell therapies, 5) regenerative medicine

China suffers from an unusually high incidence of cancer, which has been the country’s leading cause of death since 2010. Nearly all companies (five out of six) went onto HKSE in 2018 are investing in oncology (except for Hua Medicine focusing on diabetes).

Among all the cancers, lung cancer is the most targeted disease due to the high incidence rate in China.

Globally, oncology is also the No.1 focus for the overall industry, accounting for more than 1/3 of the total pipeline, according to a 2018 report[3].

To develop cancer treatments, biotech companies usually focus on developments in mAbs (monoclonal antibodies), immuno-oncology, CAR-T therapies, etc.

Other breakthrough researches & developments including Qinghaosu (or Artemisinin), discovered by the team led by Youyou Tu to fight malaria.

[1] http://classic.hsmap.com/news_info/4080.html

[2] https://hsmap.com/static/bluebook.pdf

[3] https://pharmaintelligence.informa.com/resources/product-content/sitecore/shell//~/media/informa-shop-window/pharma/files/pdfs/pharma-rd-annual-review-webinar-2018-slides.pdf

Series A-1: How biotech companies in China are funded?

 

  1. VC/PE funding

 

VC/PE funds targeting China life science investments are growing fast in recent years. According to ChinaBio, in 2018 those VC/PE funds raised around $43 billion in total and invested  around $17 billion in China life science companies, up 36% from 2017[1],.

 

The amount raised by VC/PE funds quickly ramped up during the past several years, with $10.9 billion in 2015, $20.2 billion in 2016, $39.8 billion in 2017[2].

 

Accordingly, the capital invested soared from $1-1.8 billion annually (2012-2015) to $5.4 billion in 2016, $11.7 billion in 2017 and $17.3 billion in 20182.

 

 

[One thing worth noting – many Chinese life sciences companies included/collected in ChinaBio’ research are not purely biotechnology/biopharma companies. For some VC/PE funds, for the purpose of diversification or due to other reasons, they might invest in areas other than biotechnologies.]

 

  1. IPO and capital markets

 

Similar to more developed countries like US, IPO is the most common choice for biotech companies and the funds behind them. [Another common exit opportunity is M&A, which is less likely for Chines biotech companies due to the less matured industry and capital market]

 

However, historically China’s own capital markets won’t accept most biotech companies because they are in their R&D stage with no products. Listing on China’s A-share has many requirements including reaching certain revenue and profit targets, which is very different from listing on NASDAQ. The lack of exit opportunities also (partially) explains the lack of funding in previous years. With the rise of VC/PE investments in Chinese biotech companies, appropriate exit options are needed/expected.

 

Starting from April 30 2018, Hong Kong Stock Exchange got a much anticipated listing pathway official for pre-revenue biotech companies[3],[4].

 

Five Chinese biotech companies went on HKSE via the new rule in 2018, raising nearly US$2.4 billion – Ascletis Pharma $400 million, BeiGene $903 million, Hua Medicine $114 million, Innovent $485 million, Shanghai Junshi $453 million[5],[6].

 

[The first few biotech companies listed on HKSE using the new rule are those large and “first-tier” startups; I will expect smaller IPOs in the coming years]

 

Another new board “Kechuang”, or tech board by Shanghai Stock Exchange is also going to welcome pre-revenue biotech companies starting in 2019[7]. No such listing has happened yet.

 

The capital market in China for biotech companies is still at an early stage. IPO is only one of the techniques. For example, while Nasdaq-listed biotech firms have raised US$3.5 billion from 32 post-IPO “follow-on” share issuances in the period, none has been recorded in Hong Kong yet[8].

 

  1. Public sector / state funding

 

  • Overall scale

Direct funding sources to innovations in life sciences and biotechnologies from Chinese government was said to be over ¥60 billion over the last 5 years, according to Yuanbin Wu, an officer at China’s Minister of Science and Technology, on a conference in October 2018[9].

 

Another research article published on NEJM in 2014 said China’s public sector R&D expenditure in biomedical was $2 billion in 2012[10].

 

  • Structure

A 2011 paper discussed the structure of state sponsored biotech R&D at that time[11].

NSFC = The National Natural Science Foundation of China 国家自然科学基金委员会

MOST = The Ministry of Science and Technology of the People’s Republic of China

 

There were some consolidations happening, especially for programs within MOST, which are now under one umbrella – National Key R&D Program of China (国家重点研发计划)[12].

 

And in 2018, China planned to merge NSFC under MOST[13].

 

  • NSFC

Direct supporting from NSFC totaled ~¥26 billion in 2018[14], including ¥11.2 billion available in its General Program (with ¥1.8 billion in life sciences and ¥2.5 billion in medical sciences)[15]. NSFC’s major programs are detailed below.

 

2018 National Natural Science Foundation of China (Jan 1, 2018 – Oct 24, 2018)

NSFC program names Total (all disciplines)

(¥, millions)

 Life Sciences

(¥, millions)

 Medical Sciences

(¥, millions)
General Program
面上项目		      11,152.89     1,774.7     2,521.20
Young Scientists Fund

青年科学基金项目

        4,176.44        582.40        886.80
Fund for Less Developed Regions

地区科学基金

        1,103.33        292.60        312.00
Key Program

重点项目

        2,054.42        323.00        352.70
National Science Fund for Distinguished Young Scholars杰出青年基金项目           682.85          87.50          84.00
Joint Research Fund for Overseas Chinese, Hong Kong and Macao Young Scholars海外及港澳学者合作研究基金项目             54.00            9.00            9.72
Excellent Young Scientists Fund

优秀青年基金项目

           520.00          75.40          65.00
Total      19,743.93     3,144.60     4,231.42

 

In terms of acceptance rate, for example, NSFC General Program accepted ~20% of projects across all disciplines in 2018 (specifically, life sciences 24% and medical sciences 17%). A history analysis of the fund’s overall acceptance and support is discussed in this article[16].

 

  • MOST

According to MOST’s 2018 budget, National Key R&D Program of China (国家重点研发计划) receives a budget of ~¥27.7 billion in 2018. Another program under MOST is National Science and Technology Major Project (国家科技重大专项), which receives a budget of ~43.8 million[17]. (both numbers are for all disciplines; allocation for biotech related projects is not available)

 

There are other forms of supports from both central and local governments for biotech companies in China, including tax-cut, low-cost infrastructure, etc.[18]

 

  1. Other corporate involvement

 

While China doesn’t have many big pharma companies that are financially strong, some giants in tech and insurance (Baidu, Tencent, Alibaba, PingAn, etc.) have provided certain funding to areas they are interested, usually involving digitalization, data or AI, such as genomics, diagnostics and telemedicine[19].

[1] http://www.chinabiotoday.com/articles/China-Life-Science-2018

[2] http://www.chinabiotoday.com/custom/ChinaBio_State_of_Life_Science_2019%20-%20Jan%202019%20-%20China%20Showcase%20-%20DIST(1)%20-%20Copy%201.pdf

[3] https://www.scmp.com/comment/insight-opinion/article/2143267/hkexs-new-listing-rules-will-bring-tech-economy-hong-kong

[4] https://www.hkex.com.hk/-/media/HKEX-Market/Listing/Rules-and-Guidance/Listing-Rules-Contingency/Main-Board-Listing-Rules/Equity-Securities/chapter_18a.pdf?la=en

[5] https://www.hkex.com.hk/-/media/HKEX-Market/Listing/Getting-Started/HKEX-Biotech-Newsletter-Issue-1.pdf

[6] https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/finance/deloitte-cn-mna-medicine-and-biotechnology-industry-driven-by-innovative-drugs-zh-190412.pdf

[7] https://www.spglobal.com/marketintelligence/en/news-insights/trending/amoyKnMDGMXvpAJ-p0aiHA2

[8] https://www.scmp.com/business/investor-relations/ipo-quote-profile/article/3012766/shanghai-tech-board-unlikely

[9] http://www.gov.cn/xinwen/2018-10/29/content_5335500.htm

[10] http://rwjcsp.unc.edu/downloads/news/2014/20140102_NEJM.pdf

[11] https://hal.archives-ouvertes.fr/hal-00592303/document

[12] https://baike.baidu.com/item/%E5%9B%BD%E5%AE%B6%E9%87%8D%E7%82%B9%E7%A0%94%E5%8F%91%E8%AE%A1%E5%88%92/19395314

[13] http://www.nsfc.gov.cn/csc/20340/20289/24107/index.html

[14] http://www.xinhuanet.com/2019-03/27/c_1124287185.htm

[15] http://www.nsfc.gov.cn/nsfc/cen/xmtj/pdf/2018_table.pdf

[16] http://www.nsfc.gov.cn/csc/20345/20348/pdf/2018/201802150.pdf

[17] http://www.most.gov.cn/mostinfo/xinxifenlei/czyjs/201804/P020180413411369061914.pdf

[18] https://www.hsmap.com/static/%E3%80%8A%E4%B8%AD%E5%9B%BD%E7%94%9F%E7%89%A9%E5%8C%BB%E8%8D%AF%E4%BA%A7%E4%B8%9A%E5%8F%91%E5%B1%95%E8%93%9D%E7%9A%AE%E4%B9%A6%E3%80%8B.pdf

[19] https://www.ubs.com/global/en/wealth-management/chief-investment-office/our-research/discover-more/2018/china-biotech/_jcr_content/mainpar/toplevelgrid_738393885/col2/linklist/link.0452222404.file/bGluay9wYXRoPS9jb250ZW50L2RhbS9hc3NldHMvd20vZ2xvYmFsL2Npby9kb2MvY2hpbmEtYmlvdGVjaC1yZXZvbHV0aW9uLWVuZ2xpc2gtZXgtdXMucGRm/china-biotech-revolution-english-ex-us.pdf

「Video of the Week」How deep learning is poised to transform the life sciences and healthcare

Applying deep learning in the life sciences
Deep-learning algorithms have proven to be powerful across many aspects of the life sciences and healthcare. Here are eight examples of how they’re being applied throughout the pipeline:

  1. Deep-learning-powered lead optimization: Lead optimization is a key problem. Once you’ve gotten your lead, how do you explore a 1080 space to find a better molecule based on your lead? According to a recent publication, a type of deep-learning engine called an “encoder” can encode structures with a discrete library into a continuous gradient space. That allows you to explore a much larger area of compound space than you have in your discrete library. You can then drop novel molecules out of the gradient space, design small compounds and test them to see how they perform, compared with all of the molecules in your existing library.
  2. Predicting compound activity: One variant of deep learning is known as “one-shot deep learning,” in which you train the algorithm to identify differences, rather than similarities, in data. That requires a lot less training data, which is a critical advantage. A recent paper shows that one-shot deep-learning approaches excel in predicting compound bioactivity based on training with a small set of data. So, if you’re in a new space and don’t know much about the activity of the compounds you’re using, you can still apply these approaches to get a reading of a novel molecule.
  3. Cell assay imaging analytics: Applying deep learning to cell assay imaging is an obvious path to pursue. All of the papers that have been published in this space so far have shown that deep-learning algorithms can do as well as or better than humans in detecting things like phenotypes. Better yet, they’re more efficient. You still need a human who has to be trained initially and can understand the outcomes. But overall, people are freed up to think about higher-level matters, rather than sitting there looking at images.
  4. Toxicity prediction: Deep learning has been shown to be significantly more effective than existing methods in predicting the toxicity of any given molecule. Over time, it learns to look for the specific elements or substructures that are causing the toxicity.
  5. Counterfeit scanning: Deep-learning systems can be trained to detect counterfeit drugs on the web and other sources by examining the package labeling or the pills themselves. They can pick up on little differences between real and counterfeit with a pretty high degree of accuracy. For example, logos or lettering printed on packaging can be slightly off because the presses aren’t exactly the same as the original.
  6. Electronic health record (EHR) analysis: Deep-learning approaches have been shown to significantly outperform traditional methods in doing things like patient cohort identification, readmissions analysis, clinical trial recruitment and clinical predictive modeling from EHR data stores.
  7. Language translation: Clinical trial protocol translation is a critical issue. If you provide a protocol in one language and then have someone translate it into another language, how do you know if they have translated all of the important phrases accurately? You can use deep learning to translate back into the original language and figure out how to normalize all those key criteria in your clinical trial.
  8. Electronic laboratory notebook (ELN) analysis: Many life sciences companies know they have a huge amount of information in their ELNs, but no good way of getting it out. You can have deep-learning systems go in and pull out this information; they can literally read the text in an ELN and figure out what it means.

How deep learning is poised to transform the life sciences and healthcare

 

Hong Kong Biotech IPOs – How Are They Doing

Filing Date Prospectus Date
HKG:1672 Ascletis Pharma Inc 歌礼制药 05/07/2018 7/20/2018
HKG:2552 Hua Medicine 華領醫藥 06/06/2018 8/31/2018
HKG:1801 Innovent Biologics Inc 信達生物 06/28/2018 10/18/2018
HKG:6185 Cansino Biologics Inc 康希諾生物 7/17/2018 3/18/2019
HKG:6160 Beigene Ltd 百濟神州 7/24/2018 7/30/2018
HKG:1877 Shanghai Junshi Biosciences Co Ltd 君實生物 08/06/2018 12/11/2018
HKG:2616 CStone Pharmaceuticals 基石藥業 11/11/2018 2/14/2019

FDA’s Updated Biosimilars Naming Policy

Two days after Mr. Gottlieb announced his departure from the FDA within a month on March 5, he released an updated draft guidance on biologics naming policy, adding another accomplishment in the last month of his tenure.

The question at the core is how to market “generic versions” of biologics, aka biosimilars. Unlike generic versions of traditional drugs, which could achieve a very confident level of equivalence to their original forms, the difference between biosimilars and their originals are “theoretically” high.

A biosimilar is a biological product that is highly similar to and has no clinically meaningful differences from an existing FDA-approved reference product.

– FDA

The naming policy comes into play to assert that difference.

For interchangeable biosimilars, the agency will designate a proper name that combines the core name and a distinguishing suffix that is devoid of meaning and composed of 4 lowercase letters.

– FDA

It is another confirmation that, biosimilars will not provide the similar competition as generics [to traditional drugs].

Biosimilars can be seen as lower-priced branded drugs. And it will really need real-world experiences to tell the interchangeability and other considerations.

A new biosimilar approval could be seen as providing a new solution, instead of providing a less expensive version of the current solution, to patients.

「Video of the Week」CRISPR-edited Bacteria As A Storage of Data

First video stored in its digital form in bacteria’s DNA, with the help from CRISPR/Cas9 gene editing.

DNA as a storage is not an entirely new idea. Microsoft has been exploring this field with Twist at least since 2016. They have successfully stored music (audio) performances in DNA in 2017.

Now (in the video), researchers at Harvard Medical School and the Wyss Institute are using live organisms.

「Podcast of the Week」Dog and Human Oncology Connection

Veterinary oncology can be very informative and unveil some otherwise unseen connections and undiscovered research path.

For one thing, pets are exposed to the similar environment as their human owners.

Also their immune systems are better a research/drug development target than lab mice.

And eventually, we will need cancer drugs for pets. They could be developed along with drugs for humans.

And cancer is more than a genetic mutation. It is a systematic disease and needs a comprehensive context study.