Background: The purpose of this paper is to provide technology trends and information regarding market and prospects in stents used for human blood vessels in Korea and the world. A stent is a medical device in the form of a cylindrical metal net used to normalize flow when blood or other bodily fluids such as biliary fluids are obstructed in blood vessels, gastrointestinal tracts, etc. by inserting the stent into a narrowed or clogged area. Stents are classified into vascular and non-vascular stents. The coronary artery stent is avascular stent that is used for coronary atherosclerosis. The demand is increasing for stents to treat diseases such as those affecting the heart and blood vessels of elderly and middle-aged patients. Due to the current shift in the demographic structure caused by an aging society, the prospect for stents seems to be very bright. The use of a stent designed to prevent acute vascular occlusion and restenosis, which is a side effect of conventional balloon angioplasty, has rapidly become popular because it can prevent acute complications and improve clinical outcomes. Since the initial release of this stent, there have been significant developments in its design, the most notable of which has been the introduction of drug-eluting stents (DES). Bioresorbable scaffolds (BRS) have the potential to introduce a paradigm shift in interventional cardiology, a true anatomical and functional “vascular restoration” instead of an artificial stiff tube encased by a persistent metallic foreign body. Methods: Data for this research were gathered from primary and secondary sources as well as the databases of the Korea Institute of Science Technology Information (KISTI) located in Seoul, Korea like KISTI Market Report. The sources used for primary research included the databases available from the Korea Institute of Science Technology Information, past industry research services/studies, economic and demographic data, and trade and industry journals. Secondary research was used to supplement and complement the primary research. Interviews were conducted with physicians and surgeons from the key hospitals and senior sale/marketing managers from stent product suppliers in South Korea. Results: The global stent market is estimated at US $ 7.98 billion in 2016 and is expected to grow at a Compound Annual Growth Rate (CAGR) of 3.8% over the next 5 years. As of 2016, the global market for vascular stents is estimated at $ 7.22 billion, with coronary artery stents accounting for 67.3% of the vascular stent market. Among the coronary artery stents, BRS is notably expected to grow at an annual average rate of 8.8% by 2020, but the global adoption rate of BRS remains low at present. In the Korean market, stents for blood vessels account for most of the market, and the market size of stents for blood vessels in Korea was estimated to be 145 billion won as of 2016. Conclusions: In comparison to the sales growth rate of other medical devices, the future stent technology market is judged to be higher in growth potential.

Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data occurring at manufacturing sites. To identify the threshold of the abnormal pattern requires collaboration between data analysts and manufacturing process experts, but it is practically difficult and time-consuming. This paper suggests how to derive the threshold setting of the abnormal pattern without manual labelling by process experts, and offers a prediction algorithm to predict the potentials of future failures in advance by using the hybrid Convolutional Neural Networks (CNN)–Long Short-Term Memory (LSTM) algorithm, and the Fast Fourier Transform (FFT) technique. We found that it is easier to detect abnormal patterns that cannot be found in the existing time domain after preprocessing the data set through FFT. Our study shows that both train loss and test loss were well developed, with near zero convergence with the lowest loss rate compared to existing models such as LSTM. Our proposition for the model and our method of preprocessing the data greatly helps in understanding the abnormal pattern of unlabeled big data produced at the manufacturing site, and can be a strong foundation for detecting the threshold of the abnormal pattern of big data occurring at manufacturing sites.

Technology finance, which has attracted worldwide attention for the successful business development of small-and-medium enterprises (SMEs) or start-ups, has advanced an innovation or stagnation way-out resolution strategy for companies in line with the low-growth economic trends. Although the development of new technologies and the establishment of active R&D and commercialization strategies are essential factors in a company’s management sustainability, the activation of the technology market in practice is still in progress for its golden age. In this study, to promote a technology transfer-based company’s growth and to run technology-based various financial support activities, we develop and propose a new intelligent, deep learning-based technology valuation system that enables technology holders to estimate the economic values of their innovative technologies and further to establish a firm’s commercialization strategy. For the last years, the KIBO Patent Appraisal System (KPAS-II) herein proposed has been advanced by KIBO as a web-based, artificial intelligence (AI) and evaluation data applications valuation system that automatically calculates and estimates a technology’s feasible economic value by utilizing both the intrinsic and extrinsic index information of a patent and the commercialization entity’s business capabilities, and by applying to the discounted cash flow (DCF) method in valuation theory, and finally integrating with deep learning results based on the in-advance previously established patent DB and the financial DB. The KPAS-II proposed in this study can be said to have dramatically overcome the long-term preparation period and high levels of R&D and commercialization costs in terms of the limitations that the existing technology valuation method possesses by enhancing the reliability of approximate economic values from the deep learning results based on financial data and completed valuation data. In addition, it is expected that technology marketing coordinators, researchers, and non-specialty business agents, not limited to valuation experts, can easily estimate the economic values of their patents or technologies, and they can be actively utilized in a technology-based company’s decision-making and technologically dependent financial activities.

The financial valuation of a drug that is still under development is required for various purposes. The risk-adjusted net present value (r-NPV) method, which recently emerged in the biotech industry, uses the development attrition rate as a discount factor to reflect risk during each development phase. The r-NPV method was developed to overcome the disadvantages of the prevailing discounted cash flow and real options methods and considers drug type, as well as the stage of development in its approach. Using this method, the current study examines technology values in the biopharmaceutical industry and matches the clinical development periods and success rates of these new drugs by analyzing datasets from ClinicalTrials.gov and MedTrack DB. It thus provides support for an empirical valuation model for experts in the field. Notably, there is limited research on the attrition rate and development period of new substance drugs and the research results are not consistently presented. In addition to new substance drugs, further research is necessary to deepen understanding of the attrition rate and development period of biologically-based drugs because of their inherent physical and developmental differences. Similarly, research on performance specifics within drug class models would enable refinement of the model.

This research seeks to answer the basic question, “How can we build up the formula to estimate the proper royalty rate and up-front payment using the data I can get simply as input?” This paper suggests a way to estimate the proper royalty rate and up-front payment using a formula derived from the regression of historical royalty dataset. This research analyzes the dataset, including the royalty-related data like running royalty rate (back-end payments) and up-front payment (up-front fee + milestones), regarding drug candidates for specific drug classes, like anticancer or cardiovascular, by regression analysis. Then, the formula to predict royalty-related data is derived using the attrition rate for the corresponding development phase of the drug candidate for the license deal and the revenue data of the license buyer (licensee). Lastly, the relationship between the formula to predict royalty-related data and the expected net present value is investigated. For the anticancer (antineoplastics) and cardiovascular drug classes, the formula to predict the royalty rate and up-front payment is as follows. $$ \mathrm{X}=\left(\mathrm{Attrition}\kern0.5em \mathrm{Rate}\kern0.5em \ast \kern0.5em \mathrm{Licensee}\kern0.5em \mathrm{Revenue}\right)/100 $$ $$ <\mathrm{Drug}\kern0.5em \mathrm{Class}:\kern0.5em \mathrm{Anticancer}\kern0.5em \mathrm{activity}\kern0.5em \mathrm{candidates}> $$ (1) $$ \begin{array}{l}\mathrm{Royalty}\kern0.5em \mathrm{Rate}=\left(1+\mathrm{a}*\mathrm{X}\right)/\left(\mathrm{b}+\mathrm{c}*\mathrm{X}\right)=\\ {}\left(1+\hbox{--} 5.14147\mathrm{E}\hbox{-} 09*\mathrm{X}\right)/\left(0.128436559+\hbox{--} 6.37\mathrm{E}\hbox{--} 10*\mathrm{X}\right)\end{array} $$ (2) $$ \begin{array}{l}\mathrm{Upfront}\kern0.5em \mathrm{payment}\kern0.5em \left(\mathrm{Up}\hbox{-} \mathrm{front}+\mathrm{Milestones}\right)=\left(\mathrm{a}+\mathrm{X}\right)/\left(\mathrm{b}+\mathrm{c}*\mathrm{X}\right)=\\ {}\left(\hbox{--} 133620928.7+\mathrm{X}\right)/\left(\hbox{--} 3.990489631+2.04191\mathrm{E}\hbox{--} 08*\mathrm{X}\right)\end{array} $$ $$ \mathrm{X}=\left(\mathrm{Attrition}\ \mathrm{Rate}\ *\ \mathrm{Licensee}\ \mathrm{Revenue}\right)/100 $$ $$ <\mathrm{Drug}\ \mathrm{Class}:\ \mathrm{Cardiovascular}\ \mathrm{activity}\ \mathrm{drug}\ \mathrm{candidates}> $$ (3) $$ \begin{array}{l}\mathrm{Royalty}\kern0.5em \mathrm{Rate}=\mathrm{y}0+\mathrm{a}/\mathrm{X}+\mathrm{b}/{\mathrm{X}}^2=\\ {}9.26\mathrm{e}+0+\left(-8.528+5\right)/\mathrm{X}+1.744\mathrm{e}+10/{\mathrm{X}}^2\end{array} $$ (4) $$ \begin{array}{l}\mathrm{Upfront}\kern0.5em \mathrm{payment}\kern0.5em \left(\mathrm{Up}\hbox{-} \mathrm{front}+\mathrm{Milestone}\right)=\mathrm{y}0+\mathrm{ax}+\mathrm{b}{\mathrm{x}}^2\\ {}=7.103\mathrm{e}+6+\left(\hbox{--} 3.990489631\right)*\mathrm{X}+\left(\hbox{--} 1.536\mathrm{e}\hbox{--} 12\right)*{\mathrm{X}}^2\end{array} $$ In the case of Equations Equation 2 and Equation 4, it is statistically meaningful (R2: 039–0.41); however, in the case of Equations Equation 1 and Equation 3, it has a weak relationship (R2: 022–0.28), thus requiring further study. This research is limited to the relationship between two drug classes—anticancer (antineoplastics) and cardiovascular—and royalty-related data. Valuation for the drug candidate within a specific drug class can be possible, and the royalty rate can be a variable according to drug class and licensee revenue.

This research aimed to build a solid basis through analytic hierarchy process (AHP) analysis to develop a reliable and practical valuation model that reflects the characteristics of the biotech industry and propose a reference formula to estimate the license fee by drug class for potential business transactions. In this study, we reviewed 135 related studies and found 167 related determinants. We surveyed 25 or more specialists in the biopharmaceutical industries. The survey group consisted of National Research Institutes (‘Group 1’), Companies (‘Group 2’), and Government Agencies–Universities (‘Group 3’). The average of the total group and Group 3 showed the same tendency at a Level 3 ranking, where the priority in determining the license fee was arranged in the order of ‘the market factor, the technology factor, the financial factor, and the environmental factor’ in light of the factors, and ‘patent characteristics, licensee characteristics, and licensor characteristics’ for the characteristics. We noted that the patent characteristics were primarily significant in technology transactions and their contract fee in the groups (Total, Group 2 and Group 3), followed by licensee characteristics. In terms of the in-depth index, we noted that the development phase and attrition rate, intellectual property tradability, and licensee licensing experience, followed by quality of technology, were the most influential determinants.