scholarly journals Drug Discovery Firms and Business Alliances for Sustainable Innovation

2021 ◽  
Vol 13 (7) ◽  
pp. 3599
Author(s):  
Yoshimi Harada ◽  
Huayi Wang ◽  
Kota Kodama ◽  
Shintaro Sengoku
Keyword(s):  
Drug Discovery ◽  
Initial Public Offering ◽  
New Drugs ◽  
Pharmaceutical Companies ◽  
Pharmaceutical Firms ◽  
Significant Research ◽  
Public Offering ◽  
Platform Leadership ◽  
Available Information ◽  
Startup Firms

Biotech startup firms developing pharmaceutical seeds from scientific and technological innovation are burdened by significant Research & Development (R&D) expenses, long-term R&D operations, and low probability of R&D success. To address these challenges while sustainably creating innovations and new drugs, business alliances with existing pharmaceutical companies are one of the most important issues on the management agenda. The present study explores the necessity and significance of business alliances with pharmaceutical companies for the development of drug-discovery by Japanese biotech startup firms under high uncertainty. This study investigates the types of alliances to understand the origins of sustainability of these creative activities. First, we investigate and analyze the details of the partnership and its impact on the products under development based on the publicly available information of 16 drug discovery biotech startup firms in Japan that had become public since 2010. As a result, all firms continued their operations with the funds obtained from the business alliances with pharmaceutical firms at the time of their initial public offering (IPO). In addition, 56% of these firms’ alliance projects (n = 73) were seeded-out, and 32% seeded-in, indicating that they had adopted flexible alliance strategies not limited to seed-out ones. For sustainable going concern of the biotech startup business, it is valuable to consider multiple strategic options: “in-licensing and value up”, “best-in-class”, “platform leadership” and “first-in-class” depending on the characteristics of seeds and environmental restrictions.

scholarly journals Application of Computational Biology and Artificial Intelligence Technologies in Cancer Precision Drug Discovery

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Nagasundaram Nagarajan ◽  
Edward K. Y. Yapp ◽  
Nguyen Quoc Khanh Le ◽  
Balu Kamaraj ◽  
Abeer Mohammed Al-Subaie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Computational Biology ◽  
New Drugs ◽  
Pharmaceutical Companies ◽  
Data Sets ◽  
Aggregated Data ◽  
Usable Knowledge ◽  
Knowledge Of Cancer ◽  
Generation Sequencing

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into “usable” knowledge. Being well aware of this, the world’s leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.

Blockbuster Drugs

2014 ◽  
Author(s):  
Jie Jack Li
Keyword(s):  
Drug Discovery ◽  
Pharmaceutical Industry ◽  
Prescription Drugs ◽  
Business Models ◽  
Scientific Discovery ◽  
New Drugs ◽  
Pharmaceutical Companies ◽  
Blockbuster Drug ◽  
A Minor ◽  
Future Landscape

For the world's largest prescription drug manufacturers, the last few years have been a harrowing time. Recently, Pfizer's Lipitor, GlaxoSmithKline's Advair, AstraZeneca's Seroquel, and Sanofi-Aventis and Bristol-Myers Squibb's Plavix all came off patent in the crucial U.S. market. This so-called "patent cliff" meant hundreds of billions of dollars in lost revenue and has pharmaceutical developers scrambling to create new drugs and litigating to extend current patent protections. Having spent most of his career in drug discovery in "big pharma," Dr. Li now delivers an insider's account of how the drug industry ascended to its plateau and explores the nature of the turmoil it faces in the coming years. He begins with a survey of the landscape before "blockbuster drugs," and proceeds to describe how those drugs were discovered and subsequently became integral to the business models of large pharmaceutical companies. For example, in early 1980s, Tagamet, the first "blockbuster drug," transformed a minor Philadelphia-based drug maker named SmithKline & French into the world's ninth-largest pharmaceutical company in terms of sales. The project that delivered Tagamet was nearly terminated several times because research efforts begun in 1964 produced no apparent results within the first eleven years. Similar stories accompany the discovery and development of now-ubiquitous prescription drugs, among them Claritin, Prilosec, Nexium, Plavix, and Ambien. These stories, and the facets of the pharmaceutical industry that they reveal, can teach us valuable lessons and reveal many crucial aspects about the future landscape of drug discovery. As always, Dr. Li writes in a readable style and intersperses fascinating stories of scientific discovery with engaging human drama.

scholarly journals Microarrays and NGS for Drug Discovery

2021 ◽  
Author(s):  
Laura-Ancuta Pop ◽  
Oana Zanoaga ◽  
Paul Chiroi ◽  
Andreea Nutu ◽  
Schuyler S. Korban ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Repositioning ◽  
Target Identification ◽  
New Drugs ◽  
Response To Treatment ◽  
Novel Technologies ◽  
Mutational Status ◽  
Therapeutic Guidelines ◽  
Droplet Pcr ◽  
Available Information

Novel technologies and state of the art platforms developed and launched over the last two decades such as microarrays, next-generation sequencing, and droplet PCR have provided the medical field many opportunities to generate and analyze big data from the human genome, particularly of genomes altered by different diseases like cancer, cardiovascular, diabetes and obesity. This knowledge further serves for either new drug discovery or drug repositioning. Designing drugs for specific mutations and genotypes will dramatically modify a patient’s response to treatment. Among other altered mechanisms, drug resistance is of concern, particularly when there is no response to cancer therapy. Once these new platforms for omics data are in place, available information will be used to pursue precision medicine and to establish new therapeutic guidelines. Target identification for new drugs is necessary, and it is of great benefit for critical cases where no alternatives are available. While mutational status is of highest importance as some mutations can be pathogenic, screening of known compounds in different preclinical models offer new and quick strategies to find alternative frameworks for treating more diseases with limited therapeutic options.

Do Venture Capital Investors Learn from Public Markets?

2021 ◽  
Author(s):  
Bibo Liu ◽  
Xuan Tian
Keyword(s):  
Venture Capital ◽  
Initial Public Offering ◽  
Stock Prices ◽  
Public Markets ◽  
The Public ◽  
Real Effects ◽  
Public Market ◽  
Variable Approach ◽  
Public Offering ◽  
Startup Firms

We examine whether venture capital (VC) investors learn information contained in public market stock prices. VCs are less likely to stage finance startups and syndicate with other VCs when stock prices are more informative. An instrumental variable approach suggests that the relation is likely causal. The startup’s initial public offering (IPO) prospect is the plausible information contained in stock prices learned by VCs. The effect of VC learning on staging and syndication is more pronounced when collecting information is more costly and the information learned is more reliable. Evidence from a survey of VC investors confirms that they actively learn information from the public market. VCs’ learning from the public market significantly affects their investments across startup firms. Our paper sheds new light on the real effects of financial markets and suggests that the informational role of security prices is much broader than what we have thought. This paper was accepted by Gustavo Manso, finance.

Putting the Clock Back: MicroStrategy, Inc.

2004 ◽  
Vol 19 (2) ◽  
pp. 249-260 ◽  
Author(s):  
Patricia A. Williams ◽  
Bruce S. Koch
Keyword(s):  
Stock Market ◽  
Initial Public Offering ◽  
Research Group ◽  
Stock Price ◽  
High Tech ◽  
Red Flags ◽  
Wall Street ◽  
Public Offering ◽  
Available Information ◽  
Financial Research

MicroStrategy, Inc. was one of the high-tech “darlings” of Wall Street during the stock market boom of the late 1990s. After its initial public offering (IPO) in 1998, revenue and earnings increased steadily and substantially year after year. By early March 2000, the company's stock price had soared to $333 per share. Nonetheless, there was at least one financial research group that questioned whether MicroStrategy's performance justified its high market valuation. Based on publicly available information at the time, you are asked to identify “red flags” (i.e., warnings) of possible problems with the company.

Pengaruh Reputasi Underwriter, Umur Perusahaan, dan Persentase Penawaran Saham 53 kepada Publik terhadap Underpricing

2016 ◽  
Vol 8 (1) ◽  
pp. 53-74
Author(s):  
Maria Jeanne ◽  
Chermian Eforis
Keyword(s):  
Initial Public Offering ◽  
Stock Price ◽  
Stock Exchange ◽  
Secondary Data ◽  
Secondary Market ◽  
Financial Statement ◽  
Data Set ◽  
Multiple Regression Method ◽  
Underwriter Reputation ◽  
Public Offering

The objective of this research is to obtain empirical evidence about the effect of underwriter reputation, company age, and the percentage of share’s offering to public toward underpricing. Underpricing is a phenomenon in which the current stock price initial public offering (IPO) was lower than the closing price of shares in the secondary market during the first day. Sample in this research was selected by using purposive sampling method and the secondary data used in this research was analyzed by using multiple regression method. The samples in this research were 72 companies conducting initial public offering (IPO) at the Indonesian Stock Exchange in the period January 2010 - December 2014; perform initial offering of shares; suffered underpricing; has a complete data set forth in the company's prospectus, IDX monthly statistics, financial statement and stock price site (e-bursa); and use Rupiah currency. Results of this research were (1) underwriter reputation significantly effect on underpricing; (2) company age do not effect on underpricing; and (3) the percentage of share’s offering to public do not effect on undepricing. Keywords: company age, the percentage of share’s offering to public, underpricing, underwriter reputation.

APAKAH FAKTOR EKSTERNAL MEMPREDIKSI UNDERPRICING LEBIH BAIK DIBANDINGKAN FAKTOR INTERNAL? STUDI INITIAL PUBLIK OFFERING DI BEI TAHUN 2009-2017

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Saefudin Saefudin ◽  
Tri Gunarsih
Keyword(s):  
Interest Rates ◽  
Initial Public Offering ◽  
Stock Exchange ◽  
Sample Selection ◽  
Secondary Data ◽  
Secondary Market ◽  
Company Size ◽  
Negative Effect ◽  
Underwriter Reputation ◽  
Public Offering

Underpricing is a phenomenon that still occurs in the Indonesian capital market, where the offering price of shares in the primary market is lower than the opening price or closing price on the first day on the secondary market. This study aims to examine the effect of Return On Assets (ROA), Debt to Equity Ratio (DER), company size, underwriter reputation, age, and interest rates on the underpricing of shares in companies’s Initial Public Offering (IPO) listing on the Indonesia Stock Exchange (BEI) in 2009 to 2017. The population in this study are companies that conduct IPOs on the BEI period 2009 to 2017. The sample selection in this study uses a purposive sampling method, based on certain criteria. The sample in this study were 183 underpricing companies from 205 companies conducting IPO in the period 2009 to 2017. The data used in this study used secondary data. The multiple regression analysis was implemented in this study. The results showed that DER, company size, and underwriter reputation did not significantly influence underpricing. While ROA, age and interest rates have a significant negative effect on underpricing. In this study, investors consider ROA, age, interest rates compared to DER, company size, and the reputation of the underwriter to invest in companies that make an IPO.Keywords: Underpricing, Initial Public Offering, and Indonesian Stock Exchange.

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