Pharmaceutical-Biotechnology R&D: Technological Performance Implications JONGWOOK KIM Western Washington University College of Business and Economics

Introduction: Research question How do different governance mechanisms – both formal and informal – impact alliance performance? How do different governance mechanisms – both formal and informal – impact alliance performance?  How do formal governance mechanisms that seek to mitigate information asymmetry impact alliance performance?  How do social ties matter for alliance performance?  How do different levels of uncertainty matter for alliance performance?

Introduction Why biotechnology alliances? Why biotechnology alliances?  It is difficult to measure alliance performance, particularly in R&D alliances  Biotechnology alliances provides an empirical context where a fairly objective indicator of alliance performance exists: Because of FDA regulations, information about intermediate stages in drug development are made public Because of FDA regulations, information about intermediate stages in drug development are made public

Biotechnology industry The empirical context The empirical context  Small biotechnology R&D firms usually have capabilities in product R&D activities (upstream activities)  Larger pharmaceutical firms (and some established biotechnology firms) supply capabilities in commercialization activities (downstream activities, i.e., manage the regulatory process, large-scale manufacturing, marketing & distribution, etc.)  Lengthy and uncertain regulatory (FDA) approval process

Biotechnology industry Key milestones in the drug development process Key milestones in the drug development process  Preclinical development: animal testing  Phase I clinical trials: human testing (for toxicity)  Phase III clinical trials: large-scale controlled experiments on potential patients (most expensive and time-consuming, 33-42% of total cost) (most expensive and time-consuming, 33-42% of total cost)

Introduction Strategic alliances Strategic alliances  Firms enter alliances to pool complementary resources to achieve common goals, but at the same time, partner firms are seeking access to types of resources not available to them  Information asymmetry problems for firms acquiring R&D on the part of client firms (usually pharmaceutical firms or larger biotechnology firms)  The level of information asymmetry and overall uncertainty of the drug development project changes as the drug development process progresses

Strategic alliances Informal responses to information asymmetry Informal responses to information asymmetry  Social ties (i.e., reputation and repeated ties) (Granovetter, 1985; Gulati, 1995; Powell, Koput, & Smith-Doerr, 1996; Shane & Cable, 2002) Formal responses to information asymmetry Formal responses to information asymmetry  Direct oversight through participation in R&D firm’s management or through formal contracts  Client firms can invest in stages by setting up intermediate milestones (Noldeke & Schmidt, 1995; Sahlman, 1990)

Theory development & hypotheses Repeated ties Repeated ties  Information asymmetry problems in alliance formation are more severe in the early stages of development The client firm cannot accurately ascertain the potential economic value of an R&D firm’s research output The client firm cannot accurately ascertain the potential economic value of an R&D firm’s research output  Repeated ties between the client firm and the R&D firm is an indication of the R&D firm’s product quality  H1: All else held constant, alliances with partner firms who had prior alliances with one another will more likely reach Phase I clinical trials

Theory development & hypotheses Reputation effects Reputation effects  Reputation effects (past alliances by the R&D firm) may also signal the R&D firm’s product quality  H2a: All else held constant, the greater the number of prior alliances that the R&D firm had been involved in, the more likely the alliance is to reach Phase I clinical trials.

Theory development & hypotheses Reputation effects Reputation effects  There may be negative effects of prior alliance experience of R&D firms: R&D firms may exploit asymmetric information by out-licensing less-promising drug compounds (Pisano, 1997; “lemons” hypothesis)  H2b: All else held constant, the greater the number of prior alliances that the R&D firm had been involved in, the less likely the alliance is to reach Phase I clinical trials.

Theory development & hypotheses Deal size Deal size  Alliances where the expected payoff is relatively large will lead to greater commitment by alliance partners  In particular, greater commitment suggests that the client firm is more likely to continue funding such an alliance The initial commitment to maintain relationship can be viewed as a real option (Kogut, 1991; Mahoney, 2005) The initial commitment to maintain relationship can be viewed as a real option (Kogut, 1991; Mahoney, 2005)  H3: All else held constant, larger the amount of funds committed to the alliance, the more likely the alliance is to reach Phase I clinical trials.

Theory development & hypotheses Oversight Oversight  R&D firms may be tempted to use research funds from the client firm to fund projects other than the specified ones (Pisano, 1990; Lerner & Malmendier, 2005)  R&D firms may derive private benefits from carrying marginally beneficial trials forward in early stages (Guedj & Scharfstein, 2004)  Direct oversight by the client firm should reduce such alliances from going forward  H4a: All else held constant, oversight by the client firm over the development process will decrease the likelihood of the alliance reaching Phase I clinical trials.

Theory development & hypotheses Milestone payments Milestone payments  Client firms fund R&D projects in stages so that future funding is contingent on performance by the R&D firm (Sahlman, 1990; Noldeke & Schmidt, 1995; Kaplan & Stromberg, 2004)  H4b: All else held constant, the presence of milestone payments will increase the likelihood of the alliance reaching Phase I clinical trials.  H4c: All else held constant, the presence of milestone payments will increase the likelihood of the alliance reaching Phase III clinical trials.

Theory development & hypotheses Control rights – intellectual property Control rights – intellectual property  Well-defined intellectual property rights (i.e., property rights on publication of research findings, patents, etc.) will make clearer each partner’s gains from the alliance  Intellectual property rights become more important in the later stages of development (information asymmetry dominates in early stage)  H5a: All else held constant, well-defined intellectual property rights will increase the likelihood the alliance reaching Phase III clinical trials

Theory development & hypotheses Control rights – manufacturing Control rights – manufacturing  The client firm is a pharmaceutical firm or an established biotechnology firm that is fully-integrated into manufacturing and distribution  Manufacturing is an especially critical control right because of strict FDA regulations (Lerner & Merges, 1998)  Efficient division of innovative labor suggests that the client firm should control this aspect of the alliance  H5b: All else held constant, the client firm’s control over manufacturing will increase the likelihood of the alliance reaching Phase III clinical trials.

Theory development & hypotheses Alliance management Alliance management  Compared to early stages of the development process, the financial stakes for later stages are higher (33-42% of total cost of drug development is spent on Phase III trials), but the technological uncertainty decreases  The client firm’s capabilities in managing the alliance process (collaborating with the R&D firm, dealing with regulatory agencies, etc.) matter more for performance in the later stages  H6: All else held constant, the client firm’s capabilities in managing the development process will increase the likelihood of the alliance reaching Phase III clinical trials.

Empirical results Data Data  rDNA database (Recombinant Capital)  SEC filings (10-K, 10-Q, etc.)  N = 169, research alliances (dyads only, ) that were analyzed by Recombinant Capital (contract analyses) Dependent variables: Dependent variables:  Technological performance (early stage) : conditional on the drug compound having reached preclinical trials, whether it reached Phase I trials (“success” if Phase I within 3 years)  Technological performance (later stage) : conditional on the drug compound having reached Phase I trials, whether it reached Phase III trials (“success” if Phase III within 5 years)

Empirical results Independent variables (and control variables) Independent variables (and control variables)  Repeated ties : number of prior alliances between the two alliance partners  Reputation effects : number of prior alliances by the R&D firms  Deal size : total estimated dollar amount (including upfront payments, equity investments, loans, milestone payments, etc.) that the client firm as agreed to transfer to the R&D firm  Milestones : dummy variable of whether the alliance has milestone payments specified in the agreement  Oversight : whether board seats (on R&D firm’s board) and/or joint committees are specified in the agreement  Intellectual property rights: whether patent rights and/or publication policies are specified in the agreement  Control variables: client firm size (sales), biotech-biotech alliance, functional scope of alliance

Early Stages: Preclinical – Phase I (Panel probit, N = 99) (1)(2)(3)(4)(5) Deal size *(0.2188)0.4463*(0.2327)0.4422*(0.2373)0.7408*(0.3913)0.8839*(0.4813) Reputation effects *(0.1701) **(0.2080) **(0.2127) *(0.2950) *(0.4048) Repeated ties (0.5681)0.9315(0.5690)1.2464*(0.6669)1.3021(0.7814) Milestones0.2366(0.4024)0.3238(0.4720)0.2838(0.5217) Joint committee *(0.7024) *(0.7512) Board seats (1.5535) Rho0.2791(0.3722)0.4412(0.3366)0.4404(0.3421)0.5959**(0.3210)0.7045**(0.2630) Log L Wald chi-sq

Later Stages: Phase I – Phase III (Panel probit, N = 70) (1)(2)(3) Publication policy *(0.5133) Patents0.4526(0.7103) Client manufacturing *(0.6033)1.0284*(0.5651) Milestones1.0498*(0.5832) Rho0.6563**(0.2468)0.6911***(0.2095)0.6350***(0.2187) Log L Wald chi-sq *p < 0.10, **p <0.05, ***p < 0.01

Empirical results First model (early stages): Preclinical to Phase I First model (early stages): Preclinical to Phase I  Repeated ties: inconclusive  Reputation effects: negative impact on performance Consistent with Pisano’s (1997) “lemons” hypothesis Consistent with Pisano’s (1997) “lemons” hypothesis Different specifications (past alliances in same technology area only, past research alliances only) are also negative Different specifications (past alliances in same technology area only, past research alliances only) are also negative  Deal size: positive (supported) impact on performance  Direct oversight: partially supported Only joint committees negatively impacts performance Only joint committees negatively impacts performance  Unobservable attributes of client firm ( rho ) from panel probit model: mixed results Mixed results of the impact of unobserved individual client firm characteristics on performance Mixed results of the impact of unobserved individual client firm characteristics on performance Client-firm effects seem to be weak, but present Client-firm effects seem to be weak, but present

Empirical results Second model (later stages): Phase I to Phase III Second model (later stages): Phase I to Phase III  Milestones: positive (supported) impact on performance Incentives in the form of milestone payments Incentives in the form of milestone payments  Property rights: partial support Publication policy clause has positive impact on performance, but patent clause is not supported Publication policy clause has positive impact on performance, but patent clause is not supported  Control over manufacturing by client firm: positive (supported) impact on performance Division of innovative labor where the client firm has more expertise Division of innovative labor where the client firm has more expertise  Unobservable attributes of client firm ( rho ) from panel probit model: supported Stronger support in second model of unobservable client firm-effects, suggesting the importance of alliance management capabilities on the part of the client firm Stronger support in second model of unobservable client firm-effects, suggesting the importance of alliance management capabilities on the part of the client firm

Conclusions Overall, as level of uncertainty inherent in the alliance setting changes, different variables seem to impact performance in different ways Overall, as level of uncertainty inherent in the alliance setting changes, different variables seem to impact performance in different ways Data suggest information asymmetry as key driver in early stages Data suggest information asymmetry as key driver in early stages  R&D firms are inherently optimistic about the prospects of their products and client firms that are able to best mitigate possible opportunism resulting from asymmetric information seem to have better alliance performance

Conclusions In later stages where alliances are in Phase I clinical trials, how the alliance is managed seems to be more important In later stages where alliances are in Phase I clinical trials, how the alliance is managed seems to be more important  Data suggests well-defined property rights and division of innovative labor, which are aspects of alliance management, are important for performance  Also, unobservable client firm effects are strong in later stages suggests that client firm heterogeneity (which may include alliance management capabilities) also partially account for variations in alliance performance