Global pharmaceutical research teams discover thousands of new chemical substances each year. Only a few become new drugs. It can take up to 15 years for a new drug to go through development and testing before reaching the market. Development and testing begins with extensive laboratory tests before being tested on humans in clinical trials. There are four stages in clinical trials. In those trials, a drug is tested on healthy volunteers, to see how it affects the body, and on sick volunteers, to see how effective the drugs are.
R&D alliances generated along the product development states. Early-stage collaborations within the biopharmaceutical industry are vital in driving innovation evolution through therapeutic and technological diversification (Belsey & Pavlou, 2005). DiMasi (2002) examines the financial benefits that can accrue to drug developers from improvements in drug development. He proposed that whether faster development times, quicker termination decisions or higher success rates derive from public policy initiatives, better management, or new technologies, the impact on R&D costs can be substantial. Ultimately, increased efficiency could result in more innovation and new therapies reaching patients sooner.
The process of basic discovery and development through new drug approval consists of discovery, preclinical and clinical development. Discovery often begins with the choice of a biochemical mechanism involved in a disease condition. Drug candidates, discovered in academic and pharmaceutical/biotech research labs, are tested for their interaction with the drug target. The early stage includes formulation, discovery, molecule issues. The pre-clinical phase represents bench (in vitro) and then animal testing, including kinetics, toxicity and carcinogenicity. In the US, an investigational new drug application (IND) is submitted to the Food and Drug Administration to obtain permission to begin the heavily regulated process of clinical testing in human subjects.
There are three phases of clinical trials: human pharmacology, therapeutic exploratory, and
therapeutic confirmatory.
The effects of technological heterogeneity on innovation speed are supposed to be different for divergent stages of collaborating products. In many cases, the technology behind earlier-stage product belongs to academic and fundamental technology, such as basic chemical structure analysis, biological identification technology, because early-stages along with the development process focus on finding active substances and exploring their toxicity, only single basic technology is needed for those products. On the contrary, it is necessary that companies integrate heterogeneous technology for developing later-stage products, because more complicated technology should be applied to examine the toxicity, safety and effectiveness during the later stages. For example, the aims for developing phase II or phase III products consist evaluating safety, appropriate dosages, potential side effects for numerous patients in the clinical trials, and complicated technology like pharmacokinetics, gene transferring, monoclonal antibodies hybridoma technique and other testing technology for clinical trials. Therefore, technological heterogeneity in R&D alliance is more important for developing later-stage products than those of earlier-stage products in order to reach innovation easily and efficiently.
H5: Among alliances which focus on new products closer to commercial stage, the technological heterogeneity between partners leads to faster innovation speed.
H6: Among alliances which focus on new products closer to commercial stage, the technological heterogeneity between partners leads to larger innovation quantity.
Likewise, partner network resource asymmetry is more crucial for developing later-stage products than those of earlier-stage products. As mentioned above, “non-matched dyads” have both wide and deep resources and capabilities, because the combination of both “search depth” and
“search scope” enable firms to achieve innovation more easily and quickly. Take a R&D alliance
made by a firm with a large network resource and a firm with a small network resource as example, the alliance has both wide and deep technology as well as divergent capabilities, experiences, and even financial supports, and these resources help partners gain innovation easily and quickly.
Hence, we argue that the stage of product affects the relationship between network resource asymmetry and innovation speed and quantity, and the network resource asymmetry is more demand for reaching innovation when the collaborating product is in the later stages.
H7: Among alliances which focus on new products closer to commercial stage, the network resource asymmetry between partners leads to faster innovation speed.
H8: Among alliances which focus on new products closer to commercial stage, the network resource asymmetry between partners leads to larger innovation quantity.
The above hypotheses are summarized in the table 1 and figure 2 ~ figure 4.
Table 1 Summary of Hypothesis
H1: An inverse U–shaped relationship is predicted between technological heterogeneity and innovation speed: the relationship between technological heterogeneity and innovation speed will be nonlinear with innovation speed increasing up to an optimal level beyond which higher levels of technological heterogeneity transfer lead to a decline in innovation speed.
H3: An inverse U–shaped relationship is predicted between network resource asymmetry and innovation speed: the relationship between network resource asymmetry and innovation speed will be nonlinear with innovation speed increasing up to an optimal level beyond which higher levels of network resource asymmetry transfer lead to a decline in innovation speed.
H5: Among alliances which focus on new products closer to commercial stage, the technological heterogeneity between partners leads to faster innovation speed.
H7: Among alliances which focus on new products closer to commercial stage, the network resource asymmetry between partners leads to faster innovation speed.
H2: An inverse U–shaped relationship is predicted between technological heterogeneity and innovation quantity: the relationship between technological heterogeneity and innovation quantity will be nonlinear with innovation quantity increasing up to an optimal level beyond which higher levels of technological heterogeneity transfer lead to a decline in innovation quantity.
H4: An inverse U–shaped relationship is predicted between network resource asymmetry and innovation quantity: the relationship between network resource asymmetry and innovation quantity will be nonlinear with innovation quantity increasing up to an optimal level beyond which higher levels of network resource asymmetry transfer lead to a decline in innovation quantity.
H6: Among alliances which focus on new products closer to commercial stage, the technological heterogeneity between partners leads to larger innovation quantity.
H8: Among alliances which focus on new products closer to commercial stage, the network resource asymmetry between partners leads to larger innovation quantity.
Figure 2. Partner Asymmetry and Innovation Speed (Hypotheses)
Figure 3. Partner Asymmetry and Innovation Quantity (Hypotheses)
Chapter 3: Methodology
These studies have extended our understanding of partner selection in strategic alliances.
Drawing on a range of perspectives, the former two studies explore the links among two factors within partnership of R&D alliance and innovative performance of alliance by empirical analyses:
we investigate the effects of technological heterogeneity (distance of technology) and network resource asymmetry (distance of network resource) on innovative speed and quantity. We also examined the indirect moderating effects of “time to market” on these relations. Furthermore, several factors, including alliance type, prior cooperation experience, time of contract and partner type were used as control variables in this study.
The considerations motivated the choice of the biopharmaceutical industry as the setting of the study. First, biotechnology and pharmaceutical firms invest a greater percentage of sales in R&D than any other industry (Danzon, 2005). Technological innovation behavior in the biopharmaceutical industry appears more often than it does in other industries. Second, R&D alliances have become an important mechanism for drug discovery, clinical trials, development and commercialization (Audretsch & Feldman, 2003; Xu, 2006).
3.1 Data Collection
We use data from the REDCap (Research Electronic Data Capture) database to obtain essential information about R&D alliances, including partner’s name, core technology of firms, co-patent from each alliance, time of alliance, the number of alliance, size of alliance, type of parties, and clinical stage. REDCap originated out of the Vanderbilt Institute for Clinical and Translational Research. It is a web-based system for data collection. Data entry operators enter data in a web browser, either locally or from remote locations. The data is stored centrally in a secure MySQL database. The REDCap Consortium is comprised of 267 active institutional partners from Clinical
and Translational Science Awards, General Clinical Research Centers, Research Centers in Minority Institutions, and other institutions. The consortium supports this secure web application designed exclusively to support data capture for research studies. The REDCap application allows users to build and manage online surveys and databases quickly and securely, and is currently in production use or development build-status for more than 21,000 studies with over 31,000 end-users spanning numerous research focus areas across the consortium.
There are several criteria for exclusion and inclusion. Our data set analyzes the R&D alliance activity of bio-pharmaceutical firms and research institutes from 1981 to 2010. It includes all medical treatment products for human beings (drug and diagnosis reagent), and excludes all medical prevention products (medical electric devices). In addition, is limited to alliances that have only two members and excludes those alliances with three members or more. We select only alliances with at least one co-patent. Data with many missing values were excluded. Since the majority of parties belong to three categories: biotech-biotech (BB), academic-biotech (AB) and biotech-pharmaceutical (BP), only these types of alliance were included in our research.
3.2 Measures
3.2.1 Dependent Variable (1) Time to first co-patent
We count the number of years from the time of alliance to the time to first co-patent for each target alliance. Accordingly, this research used the time to first co-patent to represent the speed of innovative performance. The longer time to first co-patent the slower a company’s innovative performance.
Time to first co-patent = The year of alliance -- The year of first co-patent
(2) Number of Co-patents
A patent represents a company’s capability of innovation, technology and production (Griliches, 1990). For this reason, number of patent is a reliable indicator of innovative performance. Since this research is about alliances, not firms, the research used the number of co-patents as a measurement of the quantity innovative performance of alliance.
3.2.2 Independent Variables (1) Technological heterogeneity
This research used the data of main technology category for each organization in RECAP database to recognize the discrepancy in technology between partners within the alliance. Five levels of technological heterogeneity were classified: low, lower medium, medium, higher medium and high technological. Since RECAP database uses the name of technology rather than technical code system to identify different technologies of firms, and there is no linkage between the name of technology and technical code in other database, we used the following criteria and process which was agreed upon by experts in the biotech and pharmaceutical technology fields to identify and categorize partners’ technology. First, each technology was divided into biotech and synthetic two groups. The scores from one to three were given to those partners’ technology that belonged to the same group, otherwise, scores from three to five were given. Second, biotechnology was subdivided into two subgroups: basic technology (ex. DNA, RNA, proteins, peptides, monoclonal antibody…) and applied technology (ex. stem cells, gene therapy, vaccines…); synthetic technology was subdivided into two subgroups: basic technology (ex. molecular structure, receptors/inhibitors…) and applied technology (ex. drug delivery, support anti-cancer agent, diagnosis…). According to whether or not partners’ technology are always the same (1), belong to the same subgroup (2), belong to the same group but different subgroups (3), belong to different
groups but both of them are either basic or applied technology (4), totally different groups and subgroups (5), the final score of technological heterogeneity was given to each alliance.
(2) Network resource asymmetry
The number of prior alliance (friends) an organization has is a good indicator of its external social resources. A company with more alliance (friends) has larger network scale. In this research, the distance of partners’ number of alliance was used to measure the asymmetry of network scale for each alliance. We first counted each partner’s number of alliance prior to the target alliance, and then used following formula to measure the value of network resource asymmetry.
Network resource asymmetry = √√√ | Partner A’s number of alliance prior to the target √ alliance -- Partner B’s number of alliance prior to the target alliance |
3.2.3 Moderating Variable Time to market
This research uses the stage of clinical trial of production to analyze the time to market. There are nine types of stage of clinical trial of production in RECAP database, and it is based on the process of drug development: “Formulation,” “Discovery,” “Lead Molecule,” “Preclinical,” “Phase I,” “Phase II,” “Phase III,” “BLA/NDA Filed,” and “Approved.” We assigned number 9 (long time) to 1 (short time) for to represent time to market.
3.2.4 Control Variables
(1) Alliance Type (Contract or Joint Venture)
We obtained the information about whether the alliance contains capital transaction from the RECAP database. We assigned 0 to those alliances that belonged to contract (without capital transaction), and 1 to those belonged to a joint venture (with capital transaction).
(2) Prior Cooperation Experience
This variable represents whether the target dyadic partners has had cooperation experience before the alliance. If the target alliance is the first cooperation within our collected data, we assume that they have 0 prior cooperation experience. Likewise, if the target alliance is not the first cooperation, we assume that they have at least 1 prior cooperation experience.
(3) Time of Contract
We gained the data about the year of contract for each alliance from RECAP database. Each time of contract was categorized into three groups: 1981-1990, 1991-2000 and 2001-2010. Since the variable belongs to category variable, this research took advantage of the dummy variable method before running the regression.
(4) Partner Type
In this research, there are three categories of alliance: biotech-biotech (BB), academic-biotech (AB), biotech-pharm (BP). Since the variable belongs to category variable, this research used the dummy variable before conducting the regression.