From Lab to Clinic: Effect of Academia–Industry Collaboration Characteristics on Oncology Phase 1 Trial Entry
Introduction Research and development (R&D) productivity continues to pose a substantial challenge for the pharmaceutical industry . For decades, the efficiency of new drug launches, as indicated by the number of FDA‐approved drugs per unit of R&D expenditure, has been on a downward trend across the entire sector . To enhance R&D effectiveness, the industry has adopted various strategies, including increased mergers and acquisitions, establishment of global research hubs, and a shift toward specialty or personalized medicines and biological targeting of orphan diseases . Amid these efforts to boost R&D efficiency, a clear trend has emerged: an increasing proportion of early‐stage development collaborations with academia. According to Bignami et al. , an examination of 2000–2015 data revealed the rising number and proportion of new molecular entities (NMEs) sourced from universities, a trend that is consistent across nearly all major countries: the study illustrated that universities have become the largest and most significant indirect source of knowledge for NMEs, and the number of distinct universities contributing to this effort has increased fourfold from 123 to 490. This phenomenon can be attributed to the mutually beneficial model of academia–pharma partnerships, which effectively share risks while providing a highly efficient conduit for the infusion of novel ideas . In essence, collaboration between academic institutions and industry is a pivotal approach for substantially boosting R&D efficiency by reducing the duration and effort required during the initial discovery phase of the drug development process and increasing the overall success rate of clinical trials . However, many existing studies on the impact of such business collaborations on the success rates of development have primarily focused on clinical‐stage products. There has been a significant lack of analysis on the success rates of development for discovery or preclinical‐stage products, where obtaining official information about the development situation is often challenging. Therefore, this study aims to analyze how the characteristics of collaborative agreements between universities or research institutions and companies in the early research phase influence the success rates of progressing to clinical stages, an area that has been under‐researched empirically. Previous studies that examined the effect of collaboration characteristics on development outcomes have analyzed various factors. These include the structure of the collaboration, size and experience of the partner organization, partner capabilities, number of collaborative agreements managed by the partner, or whether the collaboration is domestic or international . Existing literature on factors that affect R&D performance and clinical development outcomes has frequently analyzed key variables such as collaboration type, firm size, technology type, and therapeutic indication (Table ). In pharmaceutical and biotechnology, development often occurs within the framework of R&D collaborations among firms. Consequently, many studies have investigated how R&D outcomes vary based on whether the collaboration is with upstream firms (vertical collaboration) or same‐level firms (horizontal collaboration)  or other specific collaboration structures . The firm size has a significant influence (positive or negative) on R&D performance. Many studies have suggested that large corporations exhibit higher R&D efficiency because of more efficient resource management, structured development capabilities, and proactive approach to open innovation . Conversely, other studies have argued that small enterprises achieve higher R&D efficiency because of faster decision‐making processes, greater development focus, and streamlined operations . In addition, studies have analyzed R&D performance according to specific technological fields or modalities , and research focused on clinical development outcomes within specific therapeutic areas or indications. Notably, oncology has received significant attention because of its prominent representation in global development, resulting in numerous studies within this field . Applying the aforementioned elements to development projects within the context of collaborative relationships can help in establishing a comprehensive analytical framework that encapsulates the characteristics of the entire collaboration. In R&D performance measurement, the most prevalent metrics have been the success probabilities at each clinical stage or the overall success rate leading to regulatory approval after the partnering contract. Pammolli et al.  investigated the success probabilities at each stage of drug development from 1990 to 2013 and found that the preclinical stage exhibited the lowest success rates, ranging from 7.61% to 12.34%. Thus, when analyzed longitudinally the preclinical stage is the most critical phase affecting the final success of drug development. Despite the importance of preclinical research as the primary gateway for successful clinical development and its significant influence on overall development success rates, very few studies have examined the factors affecting the transition of compounds from the discovery or preclinical stages to clinical trials. The main reason for this is that many companies conducting early exploratory research are often private, meaning they are not required to provide regular external disclosures regarding the progress of their research, making it difficult to obtain accurate information. However, in cases of partnering agreements with external entities, companies tend to disclose relatively extensive information such as the product type, technology, target indication, partner firm size, collaboration structure, and roles of both parties, through disclosure statements or press releases, allowing for more precise data acquisition. Therefore, this study aims to analyze cases in the oncology field, which has shown one of the largest annual global healthcare expenditure and the lowest overall probability of success (PoS) among all therapeutic areas , focusing on pipelines developed by universities, research institutes, and hospitals. Specifically, we will examine partnership agreements established during the early discovery or preclinical development stages, and assess how the characteristics of these partnerships influence the success rates of entering clinical trials.   Methods 2.1 Data Acquisition A search within the global commercial database provided by Clarivate Analytics revealed that from January 1, 2015, to December 31, 2019, a total of 8420 contracts were executed within the oncology field. Of these contracts, 6046 were directly related to development activities, specifically categorized into three types: financial investment, co‐development, and licensing agreements. A total of 2374 contracts pertaining to commercialization activities unrelated to R&D, such as sales or co‐promotion, co‐marketing agreements, and contracts for manufacturing or simple service provision, were excluded. Financial investment contracts were included because these are typically aimed at securing funds for product development and are considered essential to R&D activities. Next, a total of 2575 cases were identified where the development stage of the original developer's pipeline was at a before clinical stage at the time the contract was signed. Among these, there were 704 cases in which the original developers were universities, academic institutions, or hospitals. Of these, 344 cases were in the discovery stage and 360 cases were in the preclinical stage at the time of the contract, and logistic regression analyses were conducted for each of these groups. (Figure ). 2.2 Collaboration Type In the R&D domain, partnership agreements can primarily be classified into three types: (1) financial investment, (2) co‐development, and (3) licensing agreements. Financial investment agreements typically involve a party, often an investor or a partner company, providing capital to fund R&D activities. This financial backing is essential for the development of new products or technologies and can include investments in existing assets. Co‐development partnerships are collaborations of two or more entities that jointly develop a product or technology. In these agreements, each party contributes their expertise, resources, and capabilities to advance a project. This could include sharing of data, research findings, technical know‐how, and sometimes the development‐associated costs. The end product or technology developed is then jointly owned, with revenue and profits shared according to terms agreed upon by the parties. Licensing agreements occurred when one entity (licensor) grants another entity (licensee) the right to use a specific technology or product they have developed, often in return for a licensing fee and royalties on sales. This allows a licensee to commercialize an R&D output or technologies that the licensee did not previously have, whereas the licensor benefits from the expanded use of their technology or product and a potential steady income stream from royalty payments. 2.3 Technology Drugs can broadly be divided into (1) small molecules, (2) peptides, proteins, monoclonal antibodies (mAbs), and antibody–drug conjugates (ADCs), and (3) cell or gene therapies, each with unique characteristics and mechanisms of action. The small molecules defined in this study's dataset are low molecular weight compounds with a molecular weight of 750 Da or less, which can easily penetrate cells and interact with intracellular targets. Peptides, proteins, mAbs, and ADCs represent a class of drugs known as biologics. Peptides and proteins are larger molecules that emulate natural ligands, hormones, or enzymes in the body. MAbs are engineered proteins that specifically bind to target antigens, often on the surface of cancer cells, or in autoimmune diseases, cells that should be blocked or destroyed. ADCs are a combination of a monoclonal antibody linked to a biologically active drug or toxin; they bind to specific targets on cell surfaces to deliver these agents directly to diseased cells while sparing healthy ones. Cell or gene therapies are at the cutting‐edge frontier of treatments and involve altering cells or genetic material for therapeutic use. Cell therapy involves the transfer of intact, live cells into a patient to help restore function by replacing or repairing damaged tissues or cells. Gene therapy modifies or manipulates a person's genes to treat or cure a disease by correcting a faulty gene, inactivating a malfunctioning one, or providing a new function to a cell. 2.4 Cancer Type Cancers can be categorized into solid and blood cancers based on the primary nature of the malignancy. Solid cancers form solid tumors. They originate from specific organs or tissues and are characterized by a mass of cancer cells that clump together. Examples include breast cancer, lung cancer, colorectal cancer, and brain tumors. Their diagnosis often involves imaging techniques such as computed tomography or magnetic resonance imaging that can visualize the abnormal tissue or lump. Conversely, blood cancers, or hematologic cancers, arise from the blood‐forming tissues, such as the bone marrow or immune system cells. This category includes leukemia, lymphoma, and myeloma. Unlike solid cancers, blood cancers generally do not form a tumor but affect the blood, bone marrow, and lymphatic system, leading to abnormal increases in blood cells that can be detected through blood tests. The distinction between solid and blood cancers is fundamental to oncology because it influences the approach to disease diagnosis, treatment, and monitoring. 2.5 Deal Start Year The dataset for this study includes partnering contracts that were signed within a 5‐year period from January 1, 2015, to December 31, 2019. The deal start year was used as a continuous variable to examine whether the timing of the contract signing has an impact on the success rate of entering clinical trials. 2.6 Partner Firm Size Partner companies were classified into either “large firms” (top 20) in global or “others” based on sales revenue in 2019. Based on this classification, large firms included Johnson & Johnson, Roche, Pfizer, Novartis, Merck & Co., GlaxoSmithKline, Sanofi, AbbVie, Takeda, Bayer, Bristol Myers Squibb, AstraZeneca, Amgen, Gilead Sciences, Eli Lilly, Boehringer Ingelheim, Novo Nordisk, Teva Pharmaceutical, Allergan, and Biogen. In numerous previous studies on the factors influencing R&D outcomes in the pharmaceutical and biotech sector, the revenue size of development companies has often been used as a straightforward and objective measure of the overall size of a company . 2.7 Domestic or International Partnership Partnership agreements can involve collaboration between companies located domestically or internationally, depending on the location of the partner company. Various sociocultural factors, such as language communication, culture, and regulations between the two companies, can also influence development outcomes. Therefore, the nature of the partnership—whether domestic or international—was analyzed as an independent variable to assess its impact. In existing studies, the distinction between domestic and international partnerships has been evaluated as a potential influencing factor in various analyses of partnership effects . 2.8 Success of Entering Phase 1 Clinical Trial The way to confirm that pipelines developed by universities, research institutes, or academic hospitals have progressed from the discovery or preclinical stage to the next development stage is to first check whether a Phase 1 clinical trial has begun. A grace period of 3 years following the contract signing was allowed to determine the success or failure for each development stage by observing whether the contracted pipeline entered into Phase 1 clinical trials. Development failure includes cases where the development stage was not confirmed after the contract or where progress has been halted for more than 3 years. 2.9 Statistical Analysis The point of the analysis is to evaluate how the partnership characteristics affect the success of entering the clinical stage of the project. Accordingly, the dependent variable was the success of entering Phase 1 clinical trials, while the independent variables included six factors: type of contract (financial investment, co‐development, and licensing), technology (small molecules, peptides/proteins/mAbs, and ADCs), type of cancer (solid tumors vs. hematological cancers), year of contract signing, size of the partner company (global Top 20 large firm vs. others), and collaboration region (domestic vs. international). Statistical analysis was conducted using logistic regression analysis with SPSS Statistics Software (V 19; IBM Corp., Armonk, NY).   Data Acquisition A search within the global commercial database provided by Clarivate Analytics revealed that from January 1, 2015, to December 31, 2019, a total of 8420 contracts were executed within the oncology field. Of these contracts, 6046 were directly related to development activities, specifically categorized into three types: financial investment, co‐development, and licensing agreements. A total of 2374 contracts pertaining to commercialization activities unrelated to R&D, such as sales or co‐promotion, co‐marketing agreements, and contracts for manufacturing or simple service provision, were excluded. Financial investment contracts were included because these are typically aimed at securing funds for product development and are considered essential to R&D activities. Next, a total of 2575 cases were identified where the development stage of the original developer's pipeline was at a before clinical stage at the time the contract was signed. Among these, there were 704 cases in which the original developers were universities, academic institutions, or hospitals. Of these, 344 cases were in the discovery stage and 360 cases were in the preclinical stage at the time of the contract, and logistic regression analyses were conducted for each of these groups. (Figure ).   Collaboration Type In the R&D domain, partnership agreements can primarily be classified into three types: (1) financial investment, (2) co‐development, and (3) licensing agreements. Financial investment agreements typically involve a party, often an investor or a partner company, providing capital to fund R&D activities. This financial backing is essential for the development of new products or technologies and can include investments in existing assets. Co‐development partnerships are collaborations of two or more entities that jointly develop a product or technology. In these agreements, each party contributes their expertise, resources, and capabilities to advance a project. This could include sharing of data, research findings, technical know‐how, and sometimes the development‐associated costs. The end product or technology developed is then jointly owned, with revenue and profits shared according to terms agreed upon by the parties. Licensing agreements occurred when one entity (licensor) grants another entity (licensee) the right to use a specific technology or product they have developed, often in return for a licensing fee and royalties on sales. This allows a licensee to commercialize an R&D output or technologies that the licensee did not previously have, whereas the licensor benefits from the expanded use of their technology or product and a potential steady income stream from royalty payments.   Technology Drugs can broadly be divided into (1) small molecules, (2) peptides, proteins, monoclonal antibodies (mAbs), and antibody–drug conjugates (ADCs), and (3) cell or gene therapies, each with unique characteristics and mechanisms of action. The small molecules defined in this study's dataset are low molecular weight compounds with a molecular weight of 750 Da or less, which can easily penetrate cells and interact with intracellular targets. Peptides, proteins, mAbs, and ADCs represent a class of drugs known as biologics. Peptides and proteins are larger molecules that emulate natural ligands, hormones, or enzymes in the body. MAbs are engineered proteins that specifically bind to target antigens, often on the surface of cancer cells, or in autoimmune diseases, cells that should be blocked or destroyed. ADCs are a combination of a monoclonal antibody linked to a biologically active drug or toxin; they bind to specific targets on cell surfaces to deliver these agents directly to diseased cells while sparing healthy ones. Cell or gene therapies are at the cutting‐edge frontier of treatments and involve altering cells or genetic material for therapeutic use. Cell therapy involves the transfer of intact, live cells into a patient to help restore function by replacing or repairing damaged tissues or cells. Gene therapy modifies or manipulates a person's genes to treat or cure a disease by correcting a faulty gene, inactivating a malfunctioning one, or providing a new function to a cell.   Cancer Type Cancers can be categorized into solid and blood cancers based on the primary nature of the malignancy. Solid cancers form solid tumors. They originate from specific organs or tissues and are characterized by a mass of cancer cells that clump together. Examples include breast cancer, lung cancer, colorectal cancer, and brain tumors. Their diagnosis often involves imaging techniques such as computed tomography or magnetic resonance imaging that can visualize the abnormal tissue or lump. Conversely, blood cancers, or hematologic cancers, arise from the blood‐forming tissues, such as the bone marrow or immune system cells. This category includes leukemia, lymphoma, and myeloma. Unlike solid cancers, blood cancers generally do not form a tumor but affect the blood, bone marrow, and lymphatic system, leading to abnormal increases in blood cells that can be detected through blood tests. The distinction between solid and blood cancers is fundamental to oncology because it influences the approach to disease diagnosis, treatment, and monitoring.   Deal Start Year The dataset for this study includes partnering contracts that were signed within a 5‐year period from January 1, 2015, to December 31, 2019. The deal start year was used as a continuous variable to examine whether the timing of the contract signing has an impact on the success rate of entering clinical trials.   Partner Firm Size Partner companies were classified into either “large firms” (top 20) in global or “others” based on sales revenue in 2019. Based on this classification, large firms included Johnson & Johnson, Roche, Pfizer, Novartis, Merck & Co., GlaxoSmithKline, Sanofi, AbbVie, Takeda, Bayer, Bristol Myers Squibb, AstraZeneca, Amgen, Gilead Sciences, Eli Lilly, Boehringer Ingelheim, Novo Nordisk, Teva Pharmaceutical, Allergan, and Biogen. In numerous previous studies on the factors influencing R&D outcomes in the pharmaceutical and biotech sector, the revenue size of development companies has often been used as a straightforward and objective measure of the overall size of a company .   Domestic or International Partnership Partnership agreements can involve collaboration between companies located domestically or internationally, depending on the location of the partner company. Various sociocultural factors, such as language communication, culture, and regulations between the two companies, can also influence development outcomes. Therefore, the nature of the partnership—whether domestic or international—was analyzed as an independent variable to assess its impact. In existing studies, the distinction between domestic and international partnerships has been evaluated as a potential influencing factor in various analyses of partnership effects .   Success of Entering Phase 1 Clinical Trial The way to confirm that pipelines developed by universities, research institutes, or academic hospitals have progressed from the discovery or preclinical stage to the next development stage is to first check whether a Phase 1 clinical trial has begun. A grace period of 3 years following the contract signing was allowed to determine the success or failure for each development stage by observing whether the contracted pipeline entered into Phase 1 clinical trials. Development failure includes cases where the development stage was not confirmed after the contract or where progress has been halted for more than 3 years.   Statistical Analysis The point of the analysis is to evaluate how the partnership characteristics affect the success of entering the clinical stage of the project. Accordingly, the dependent variable was the success of entering Phase 1 clinical trials, while the independent variables included six factors: type of contract (financial investment, co‐development, and licensing), technology (small molecules, peptides/proteins/mAbs, and ADCs), type of cancer (solid tumors vs. hematological cancers), year of contract signing, size of the partner company (global Top 20 large firm vs. others), and collaboration region (domestic vs. international). Statistical analysis was conducted using logistic regression analysis with SPSS Statistics Software (V 19; IBM Corp., Armonk, NY).   Results As shown in Table and Figure , the Phase 1 clinical trial entry success rates for oncology collaborative projects were 9.9% from the discovery stage and 24.2% from the preclinical stage. Especially in contracts at the discovery stage, there was a very clear difference in the probability of Phase 1 clinical entry success based on collaboration type; compared to financial investment agreements, co‐development agreements and license agreements showed differences in clinical entry success rates of 9.4% and 24.6%, respectively. Additionally, there was a statistically significant difference based on technology in the contract cases at the discovery stage, with cell or gene therapies showing a clinical entry success rate 15.7% higher than that of small molecules. In terms of cancer type, clear differences were observed in contracts at the preclinical stage, with blood cancers showing a clinical entry success probability that was 15.3% higher than that of solid cancers. The results of the logistic regression analyses for the partnering cases at the discovery and preclinical stages are summarized in Table . It is particularly noteworthy that the success rate for entry into clinical trials varied significantly based on the type of contract in the discovery stage. Co‐development and licensing deals showed significantly higher odds ratio of successfully entering clinical trials—16.45 times and 42.43 times, respectively—when compared to simple financial investment deals. In contrast, no statistically significant changes were observed for contracts at the preclinical stage. In terms of technology type, in the discovery stage, cell or gene therapies exhibited a statistically significant odds ratio; 3.82 times higher for entering clinical trials compared to that of small molecule. However, no significant changes were observed among preclinical stage contracts. When examining the impact by cancer type, no significant changes were observed for discovery stage contracts. However, in the preclinical stage, assets targeting blood cancers showed a success rate approximately 2.24 times higher than those targeting solid cancers. We also examined how the year of contract signing, treated as a continuous variable, affected the success rate of clinical trial entry. The results indicate a trend where the probability of clinical entry for contracts at the preclinical stage increases by approximately 1.24 times each year though there was no overall increase in success rates for discovery stage contracts. Interestingly, while large firms showed a trend of having a significantly lower success rate for entering clinical trials (0.29 times lower) compared to small to medium‐sized enterprises, this result was not statistically significant mainly due to its very small number of cases. Lastly, the regression analysis investigating the impact of domestic versus international partnerships on the success rates of entering clinical trials indicated no statistically significant results in both discovery and preclinical stage contracts.   Discussion This study analyzes the impact of the characteristics of partnership contracts at the early development stage on the success of entry into Phase 1 clinical trials in the field of oncology drug development. Notably, it focuses on early partnership contracts between academia and industry, an area where empirical studies have been relatively scarce. The analysis results of this study showed that the overall success rate for entering Phase 1 clinical trials from the preclinical stage was 24.2% out of 360 cases. This value is considerably higher than the 7.61%–12.34% range observed previously by Pammolli et al. . The high success rate in the present study can be attributed to the dataset being limited to drug development projects with established partnering agreements, as opposed to the broader dataset in Pammolli's study, which included both partnered and non‐partnered cases. This finding is in alignment with Takebe's study , which concluded that collaborative development between academia and industry results in higher success rates across all development stages than originator's independent development without partnership. However, the average success rate of preclinical projects for all indications observed in Takebe's study is 36.7%, still significantly higher than the figures analyzed in this study. Considering the relatively high complexity and challenges associated with oncology drug development, the observed success rate limited to oncology projects in this study is thought to be correspondingly lower. Based on this, if we apply the simple logical formula “(PoS from discovery to clinical) = (PoS in discovery stage) × (PoS in preclinical stage)”, the estimated success rate for development at the discovery stage is approximately 40.9%. Research addressing the success rates in the discovery stage empirically is quite rare. However, Paul's study  estimates the success rates for key development steps as follows: Target‐to‐Hit at 80%, Hit‐to‐Lead at 75%, and Lead Optimization at 85%. Using these figures, the success rate for the discovery stage can be estimated to be about 51%. Thus, the figure of 40.9% estimated in this study aligns closely with this broader understanding. According to the results in Table , there is a significant difference in the success rates of clinical entry based on the type of contracts at the discovery stage. The discovery stage is the initial phase of research, focused on identifying candidate substances, and carries a substantial risk of failure before advancing to preclinical studies. Securing financial investment at this early stage is considerably more challenging than attracting funding during the preclinical phase, as it demands high evaluation criteria. Therefore, in addition to simple investment contracts for funding, partnership agreements for actual technology development, such as co‐development contracts or licensing agreements that grant full rights of development and commercialization, indicate a stronger confidence from the partner in the technology. After establishing such contracts, partners are likely to concentrate their capabilities and efforts on development. In particular, in the case of licensing agreements, where the partner assumes full and sole responsibility for development and commercialization unlike co‐development agreement, the focus on advancing the project is expected to be even higher, which may lead to increased success rates for clinical entry. Moreover, the analysis of clinical success rates based on technology type within discovery stage contracts reveals that cell or gene therapies, considered the latest cutting‐edge technologies, exhibit a success rate approximately 3.82 times higher than that of small molecules. This suggests that there is heightened interest in new modalities or innovative technologies in the early discovery phase, leading to concentrated research and development efforts through partnering contracts, which ultimately results in a higher success rate for entering clinical trials. For reference, the Takebe's study  found that the preclinical development success rate for biologics (37%), considered next‐generation technology, is higher than that of small molecules (25%), seen as first‐generation technology. Similarly, although the results pertain to contracts at the discovery stage rather than the preclinical stage, this study showed that cell or gene therapies, representing cutting‐edge technologies, have a significantly higher success rate for entering clinical trials compared to small molecules. Several interpretations can be made on this. Cell or gene therapies target specific cells or genetic sequences, and make the target engaged biomarkers clearer and the mechanisms of action easier to elucidate. Clinical trials for these therapies often involve personalized treatments for patients with specific genetic issues, thereby enhancing the probability of entering Phase 1 clinical trials. On the contrary, small molecules may have higher risk of potential adverse effects due to its unpredictable interactions with various receptors or cells. This suggests that cell or gene therapies as a personalized treatment option are leading to increasing success rates toward clinical trials. In the case of preclinical stage contracts, it has been analyzed that the success rate of entering clinical trials for blood cancers is approximately 2.24 times higher than that for solid cancers. This finding aligns with previous research by Hay , which examined success rates in the clinical development phase. This difference can be attributed to the fundamental differences in tumor biology between solid tumors and hematologic malignancies, including the mechanisms of T cell action . The low clinical entry success rate observed in collaborations with global top 20 large pharmaceutical companies in this study is consistent with existing research findings. The large firms tend to have highly diversified pipeline portfolios and a substantial number of candidate molecules in development, which increases the internal competition among programs and raises the threshold for final decision on entering clinical trials . For early‐stage oncological programs in academia, the success of clinical entry is more significantly affected by the collaboration type and the nature of the technology or product rather than the size of the partner company. That is, the more the development lead and responsibilities were transferred to the partner company, ranging from simple R&D funding to co‐development and ultimately to licensing, the higher the partner's sense of accountability and dedication to the project, thereby increasing the clinical entry success rate . This insight is regarded as critically important for formulating strategies that enhance the likelihood of successful development through partnerships in fields such as oncology, where developmental challenges are substantial and considerable investment is essential. For the advancement of future research, extending such research to other therapeutic areas beyond oncology would be valuable. Investigating how factors influencing clinical entry success rates vary across indications and interpreting these findings would aid in developing optimal partnering and development strategies tailored to specific therapeutic areas.   Wonseok Yang and Sang‐Won Lee wrote the manuscript, designed the research, performed the research, and contributed new analytical tools. Wonseok Yang analyzed the data.   The authors declare no conflicts of interest.   Table S1.   