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Una oportunidad para la ciencia española.

El lunes 14 pasado se presentó en el Congreso de los Diputados la nueva Oficina de Ciencia y Tecnología del Congreso, también conocida como Oficina C. Se trata de una iniciativa conjunta del Congreso de los Diputados y la Fundación Española para la Ciencia y la Tecnología (FECYT). Al acto asistieron investigadores y parlamentarios de todos los grupos y se presentaron los cuatro primeros informes que ha preparado esta nueva oficina de asesoramiento científico para todos los diputados y diputadas, sobre ciberseguridad, hidrógeno verde, avances en el tratamiento contra el cáncer, e inteligencia artificial en salud.

Diez personas seleccionadas de todo el sistema de ciencia español asistieron también a esta presentación. Estas personas hemos estado además durante cuatro días involucrados en una actividad de intercambio de experiencias con los diputados, entre el lunes y jueves de esta semana pasada. El objetivo ha sido conocer mejor cómo es el día a día de los políticos y del proceso legislativo, y cómo la evidencia científica puede contribuir a la toma de decisiones en política. Las decisiones informadas por la ciencia tienen más posibilidades de acertar.

En el marco de este programa de presentación de la Oficina C he tenido la suerte de conocer la actividad en el Congreso de los Diputados de la mano del diputado por Álava Iñaki Ruiz de Pinedo (@RuizdePinedo). Ha sido una experiencia extraordinaria que recomiendo totalmente para futuras ediciones. Hay un consenso creo en que ha superado mucho las expectativas de tanto las diez personas relacionadas con la ciencia como de los diputados participantes.

El objetivo de la nueva Oficina C es favorecer políticas informadas por la evidencia, recoger la evidencia científica sobre temas de interés y sintetizarla para uso de todo el arco parlamentario. En las conversaciones con los diputados ha quedado claro que la política está totalmente dispuesta e interesada en tener esta información. Va a ser muy útil para mejorar el proceso legislativo, y cada vez más porque la ciencia y la tecnología progresan cada vez más rápido. El poder legislar de manera acertada y rápida con un soporte robusto en la evidencia científica tiene dos efectos muy importantes: en primer lugar, nueva legislación es necesaria para proteger al consumidor en cuestiones ya presentes en la sociedad, pero sin claridad legal. Además, el que el uso de nuevas tecnologías y avances prometedores no esté reflejados en la legislación vigente supone una incertidumbre y una barrera para continuar su desarrollo y su comercialización. Si hay una buena legislación en España en estas cuestiones en las que la evidencia científica es parte importante, las empresas españolas podrán ser pioneras y líderes en el mundo. El hidrógeno verde puede ser un ejemplo concreto de esta oportunidad única para España de unir ciencia y política.

La iniciativa ha tenido para mi un impacto inesperado, al conocer de cerca todo el trabajo que ocurre en el Congreso de los Diputados. Valoro ahora personalmente mucho más la labor y dedicación de los parlamentarios. Su señoría Ruiz de Pinedo ha sido muy accesible, y he podido compartir con él muchas conversaciones de un valor difícil de cuantificar. He visto muchas horas de trabajo y dedicación en él y en los parlamentarios con los que hemos interaccionado.

La nueva Oficina C tiene ahora la tarea compleja de convertirse en un instrumento eficaz de asesoramiento científico en el centro legislativo nacional. No se trata de prescribir qué políticas se deben adoptar, pero sí de informar sobre cuál es la realidad a la que deben responder esas políticas. La experiencia de esta semana pasada creo que va a ser muy útil para que los diputados y diputadas refuercen su conexión con el mundo científico. Además, es un modelo que puede ser transladable a otras estructuras legislativas, en particular los parlamentos regionales.

Notas sobre patentes, interés de mercado, y creación de spin-offs

Una notas sobre patentes después de asistir a la jornada del martes 18 de octubre en la UPV con representantes de la OEPM y con investigadores de la UPV, y de alguna conversación después con personas más expertas e inteligentes que yo.

>> La actividad de transferencia en España es frágil, con pocos éxitos si comparamos con los indicadores de producción científica y el esfuerzo público invertido. Nos cuesta mucho llevar innovación desde la universidad a la industria. Hay aún muchas patentes que no se licencian. En el caso de la investigación aplicada habría que tener en cuenta y cultivar el origen de las propuestas de investigación. Proteger en la medida de lo posible resultados de investigación ya alineados con un «interés de mercado». Tener en cuenta estas necesidades e interés del mercado a la hora de construir las propuestas de investigación. Como investigadores universitarios es entonces importante poner en marcha y acceder a canales y herramientas para interactuar con las empresas / la aplicación esperada de nuestra investigación. Por ejemplo, se intuye el inicio de un cambio de fondo de las políticas que buscan reforzar la actividad de transferencia en España con más actuación sobre las empresas del entorno local para activar la demanda de innovación [1]. Hay nuevos programas públicos de financiación de la I+D empresarial en los que los investigadores universitarios pueden participar y aprovechar para relacionarse más con empresas relacionadas con su campo.

>> Las patentes son una manera de poner en valor el talento, las ideas y el trabajo de los investigadores. Son una herramienta muy útil, casi siempre imprescindible, para hablar con una empresa o para crear una spin-off. Ocurre que cada vez más las empresas que realizan I+D de manera intensiva prefieren adquirir o asociarse a una empresa start-up o spin-off que ya ha desarrollado un producto y ha hecho una primera validación de mercado [2], más que adquirir una licencia sobre una patente de una tecnología que aún está en fase de prueba de concepto. Esto supongo que es variable por sectores. En el sector biotech salud diría que ya hace unos años que es mayoritariamente así. En el momento en que la nueva empresa universitaria ha conseguido demostrar que su tecnología funciona en un entorno real, con una demanda de mercado suficiente, es necesario un socio con capacidad de invertir para escalar y comercializar la tecnología, lo que especialmente en el sector de la salud suele ser una empresa mucho más grande ya establecida. Teniendo esto en cuenta los investigadores con un resultado protegible deberían considerar las opciones y los tiempos para establecer una empresa spin-off que valide la tecnología de manera más completa, con muchas más posibilidades de ser finalmente transferida a mercado. Las universidades deberían considerar que pueden ofrecer a estos investigadores , como institución, con herramientas de formación, acompañamiento profesional de construcción de una spin-off, inversión semilla, etc.

>> La decisión de cuando patentar es igual o más importante que ya lo era. Si queremos mover la creación de una spin-off los 30 meses hasta fases nacionales pasan muy rápido. Hasta que la patente no está presentada no se puede publicar nada. Ni tesis, ni trabajo final de máster, ni artículo en revista de alto impacto. Este cambio en los caminos preferidos de transferencia, con empresas que buscan tecnología más validada, a poder ser en el seno de una empresa spin-off, afecta a los tiempos en la planificación de la protección de nuevas tecnologías en el entorno universitario. Según el modelo clásico y aún mayoritario, un investigador con una nueva tecnología que empieza a desarrollar puede realizar con bastante rapidez un trabajo de validación en el laboratorio suficiente para presentar una patente, publicar a continuación un artículo científico, y tratar luego de buscar empresas interesadas en una licencia de la patente mientras se continúa trabajando en la tecnología en el laboratorio. Hay básicamente 30 meses para hacer esto sin que sea demasiado caro. Esta patente sin embargo tiene pocas probabilidades de éxito, en el sentido de ser licenciada, atendiendo a las estadísticas de estos años pasados. Si consideramos las opciones para poner en marcha la creación de una empresa spin-off basada en la tecnología patentada hay que contar con tiempo suficiente para madurar la tecnología y la empresa lo suficiente para conseguir inversores, conseguir que la empresa sobreviva los primeros meses después de su creación y pueda hacerse interesante para otras empresas ya en el mercado. Los plazos pasan rápido, y en un máximo de 30 meses desde la presentación de la patente (finalización del procedimiento PCT) es difícil dar todos los pasos si no están mínimamente planificados. A partir de ese momento una patente activa en 10-12 países costará unos 35-40 mil euros anuales de mantener. En definitiva, cuando en un grupo de investigación se empieza a desarrollar una nueva tecnología, y hay unos primeros buenos resultados experimentales, hay que pensar en si en un tiempo breve es posible apoyar la creación de una spin-off desde el grupo, o es mejor esperar a presentar la patente y continuar la validación de la tecnología en el entorno universitario, para extender al máximo el tiempo disponible, entendiendo bien lo que conlleva en cuanto a no publicar artículos científicos. Desde el punto de vista de la estrategia del investigador académico, es probable que según el estado de desarrollo de la tecnología en el momento de poder empezar a publicar haya que elegir entre patentar ya o esperar hasta tener más avanzada la demostración de la tecnología. Si se presenta la patente demasiado pronto es posible seguir trabajando en el camino de transferencia de licencia y validación en laboratorio con proyectos de prueba de concepto con financiación pública, pero quizás sea difícil establecer a tiempo una spin-off robusta, que pueda entrar en rondas de inversión, antes de que la patente entre en fases nacionales y haya que decidir abandonarla o pagar los costes significativos que se generan a partir de ese momento.

[1]  https://www.viaempresa.cat/es/economia/dura-realidad-transferencia-tecnologica_2170707_102.html

[2] https://www.universidadsi.es/empresas-spin-off-exoticas-o-habituales/

Outstanding skills of the academic researcher and how to improve them

There are several outstanding skills that academic researchers develop and use during their career, sometimes inadvertently. These are in addition to an expert knowledge of the state-of-the-art of a field, and of how the public R&D system works, which could be said that are the competences taken for granted for an academic researcher.

The usual research day-to-day activities have a big impact on key skills hugely helpful for a successful career as a scientist, in or outside of academia. Things like planning how to solve a problem with limited information, how to present results to your peers, or how to partner within a well-functioning team, are actually critical to perform successfully in many jobs. As an academic researcher you can reflect on your level on these and plan on how to improve them. Also, being aware of them and where you stand, can make a great difference when you have to talk about yourself in a job interview.

A couple of weeks ago in our lab meeting we talked about four of these skills or competences and discussed a few practical actions to improve them. As trainee or mentor, you can work actively to enhance your performance with them but also that of your team.

(i) Analysis

As an academic research you are quite used to identifying a problem, imagining possible solutions, planning work on the one that has the most chances of being successful, and defining how to check if the solution is working. You gather information about the state-of-the-art science, about available resources, your team, your facilities, and so on. Project planning, and the agility to jump into something new and get things done when the situation is ambiguous, is a highly valued skill. Even if a researcher is not aware of it, their daily job trains them for this comprehensively. This skill can also be described as a problem-solving skill, or more generally as a (strategic) analysis skill.

There are many resources online to work on this. A good and easy start point can be to read about SWOT – CAME analysis, a much used tool for long term planning that can help us be more aware of how we make decisions and visualize possible future pathways in our big projects and lines of research.

highlighted skilladvice to improve your level
Analysis (defining goals, problem solving)be aware of the process of (strategic) analysis when planning and executing your work

(ii) Excellence

There are three aspects of working in the academic research field that I believe can be said that coach you to be excellent, that is, strict with the quality of your work and that of others: first, the scientific method demands evidence, which is strong only with high quality results; second, researchers tend to specialize and be proficient on particular techniques, equipment or knowledge; and third, academic researchers tend to have flexible schedules where they can more or less organize their own work, which means they have flexibility to reflect on their own methods and priorities, and plan how to be better at them.

highlighted skilladvice to improve your level
Excellence
High thresholds for quality of resultsAsk yourself about the reproducibility of your results.
Proficiency on techniques, equipment or knowledgeFocus on improving your proficiency on particular techniques / equipment / knowledge where you can excel.
Work routines: planning and working load dynamicsAsk yourself how you organize your work and your methodology to plan tasks, daily in particular. Reflect on prioritizing. If your work (consistently) needs doing weekends/late nights, your system is flawed. If you continue to do it, then you are hiding the underlying flaws, which will not fix the problem. Sticking to a reasonable working load is key.

(iii) Communication

People may have interesting ideas but if they cannot be communicated, written down, told, in a way that is easily understood they are lost. This is critical in research as people strive to get funded by telling others about their ideas. Writing abilities for instance take a lot of practice to improve, and this must be addressed as early as possible in the career of academic researchers.

Seasoned academic researchers are usually able to write with fluency, legibility, and clarity; are able to speak in front of an audience with good articulation, expressivity, and their spoken English is very good.

highlighted skilladvice to improve your level
Communication
In written comms: fluency, legibility, clarity.Write and read a lot  (any kind of text, fiction included)
In oral comms: articulation, expressivityPractice every chance available, join a performing arts group
English: fluency, accentWatch and read as much as possible in English

(iv) Human interactions

Collaborative dynamics have been always a feature of academic research, and without numbers to prove it at hand I would say that much more now that say twenty years ago. Partnering with others to complement our knowledge and resources, team dynamics, are skills where successful researchers really excel. In Academia, as in most other jobs, you often find that circumstances or other people choose who you work with. So, you may be stuck with people and work you don’t enjoy. This happens often, but in Academia, especially if you are a PI, you are quite able to decline collaborations with people who will have a negative impact on your well-being, even if it would benefit your career. It’s not worth it. When possible, we all should only work with people we enjoy working with. This agency is one of the big advantages of academia and being aware of it and learning how to do it with elegance is also in my experience a common trait of successful researchers.

There are other very important competences related to human psychology that don’t often come with the experience of academic research, such as acknowledging diversity in personalities and backgrounds, knowing how to read others’ personalities, or establishing trust.

Outside of Academia the diversity of backgrounds in a similar role is much higher. In most academic environments most have very similar paths. Age is much less important than in academia. Maybe in Academia it would be good to plan more for activities to just get to know people, which in my opinion is much more important than most researchers imagine. As an individual researcher or as a lab it can be very useful to set up meetings with people you don’t know in the organization you work or in your field, just to get to know them. Half an hour chats are so valuable to establishing networks. That great research paper you just read? Why not send them an email to ask for a quick chat? The worst thing they can say is no.

highlighted skilladvice to improve your level
Human interactions
collaborative dynamics: partnering, team dynamicswhen possible, only work with people you enjoy working with. Decline collaborations with people who will have a negative impact on your well-being, even if you think it might benefit your career.
human psychologyacknowledge diversity, work to know oneself and others’ personalities, ask yourself what is needed to establish trust.
networkingwork on your “first-contact” and “follow-up” strategies

Post image was produced by the AI Midjourney bot

Stem cell research: for small labs it makes sense to focus on basic research, to support future better human trials.

In the last decade research funding and interest in stem cell research has moved its focus to tackle hurdles that limit clinical translation to humans, such as reliability and safety of cell sources, and their scaled-up manufacturing. A challenge remains however in the scope of basic science, to understand mechanisms of stem cell biology that have proved to be definitely more complex than maybe anticipated. This would allow better informed human trials. Several thousands of clinical trials of cell therapies have been run and only a few tens of products are currently commercialised. Basic research labs can lead this progress with more flexibility and expertise.

It is already a feature of the stem cell research field how difficult is to translate lab results into the clinic. The FDA has currently (September 2022) approved only 23 products [1], 19 the EMA [2], and this after decades of huge public and private funding. It is telling that this is over 15 years since the discovery of induced pluripotent stem cells (iPSCs) [3], that at first seemed to have a much better chance of being transformed into therapies, as they avoid ethical and sourcing problems of other stem cells types.

Big research structures such as the California Institute for Regenerative Medicine (CIRM), the UK Cell and Gene Therapy Catapult (CGTC), and others around the world were created to support the translation process and to gather relevant insights on how to improve its success. As a result, more R&D funding is now directed to regulatory assessment, preclinical models, and manufacturing, i.e., how reliable and scalable cell sources are. In terms of academic research this usually means bigger projects, access to better facilities and more resources, and partnering with industry and other stakeholders at early stages of research. The CIRM was created in 2004 to accelerate therapy development and was restructured in 2015 to align with key regulatory and product development requirements, a strategy that seems to have work for CIRM [4] to improve moves to clinical stages of research. CIRM-funded projects have reached phase III clinical trials with overall great returns to the Californian economy [5]. However, it is significant that no therapy developed with CIRM funding has come yet to market. The CGTC has also recently proposed to target manufacturing supply chains and embedding collaborations for the provision of cell and gene therapies in the UK [6].

These barriers to translation are better defined now and we could finally see many more effective therapies commercialised soon. Still, these barriers related to development, safety, scale-up stages, are not the only barriers faced by stem cell research: huge progress is to be made by understanding why so many of these cellular products fail [7]. Smaller labs such as ours with researchers specialised on regenerative medicine and basic research don’t need the resources required to work towards the preclinical and regulatory challenges noted above. They can focus on elucidating molecular biology mechanisms, for instance those that would explain why animals and cell-culture systems do not recapitulate the relevant human conditions sufficiently well, or how the safety of iPSCs (risk of oncogenic transformation) can be better measured and ensured.

AI generated artistic images produced by the Midjourney bot with the text prompt «stem cell research gustav klimt»

References

[1] FDA https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/approved-cellular-and-gene-therapy-products

[2] https://www.cell.com/molecular-therapy-family/methods/fulltext/S2329-0501(21)00175-3#tbl1

Current landscape of clinical development and approval of advanced therapies. Iglesias-Lopez, Carolina et al. Molecular Therapy – Methods & Clinical Development, Volume 23, 606 – 618.

[3] https://stemcellres.biomedcentral.com/articles/10.1186/s13287-019-1165-5

Wojciech Zakrzewski, Maciej Dobrzyński, Maria Szymonowicz & Zbigniew Rybak. Stem cells: past, present, and future. Stem Cell Research & Therapy volume 10, Article number: 68 (2019)

[4] https://www.sciencedirect.com/science/article/pii/S1934590920301028

Gilberto R. Sambrano, Maria T. Millan. Translating Science into the Clinic: The Role of Funding Agencies. Cell Stem Cell, Volume 26, Issue 4, 2020, Pages 479-481.

[5] https://healthpolicy.usc.edu/research/the-economic-case-for-public-investment-in-stem-cell-research/

The Economic Case for Public Investment in Stem Cell Research. USC Schaffer White Paper by Dana Goldman, PhD, Martha Ryan, Bryan Tysinger, PhD, Adam Rose, Dan Wei and Mark S. Humayun. Published online June 24, 2020.

[6] https://ct.catapult.org.uk/sites/default/files/publication/National%20Cell%20and%20Gene%20Therapy%20Vision%20for%20the%20UK.pdf

[7] https://www.nature.com/articles/s41551-022-00892-4

Making fitter cells and tissues. Nat. Editorial, Biomed. Eng 6, 325–326 (2022).

Como elegir un gran colaborador científico

Mi resumen y notas de una columna publicada recientemente en la revista Nature: How to pick a great scientific collaborator escrita por el profesor danés Carsten Lund Pedersen.

>Encontrar grandes colaboradores y poder trabajar con ellos de manera productiva es uno de los predictores más importantes de éxito.

>Hay tres rasgos principales de un gran socio: (i) es «alguien con quien es divertido trabajar«, (ii) es «alguien que contribuye al trabajo«, y (iii) es «alguien que tiene la misma ambición«

>A partir de estos tres rasgos se propone una herramienta o marco de análisis que clasifica a los colaboradores y a nosotros mismos en:

  • «colaboradores más valiosos«, los que cumplen con los tres rasgos.
  • «gorrones simpáticos«, con los que es divertido trabajar y tienen el mismo nivel de ambición, pero no contribuyen de manera significativa al trabajo. El autor propone que a veces es posible reconducir la colaboración con una conversación seria.
  • «productivos fastidiosos«, que son personas que contribuyen a un proyecto y comparten ambición, pero con los que no es divertido trabajar. Pueden ayudarte a progresar, pero también pueden hacerte sentir miserable en el proceso. El autor propones dos opciones: evitar a estos individuos por completo, o «protegerse» de ellos teniendo otros colaboradores más divertidos en un mismo proyecto.
  • «socios desalineados«, con los que es divertido trabajar y contribuyen al estudio, pero no tienen la misma ambición. No trabajan en el mismo tema o no tienen objetivos similares en su carrera. El autor propone si es posible encontrar proyectos comunes en los que se tenga el mismo nivel de ambición.

>El autor propone finalmente el uso de la herramienta en uno mismo, como autoevaluación para entender cómo nos ven nuestros colaboradores y poder mejorar nuestra interacción con ellos. El autor utiliza la herramienta para describir que que en el pasado sus rasgos han sido a veces los de un socio de tipo «productivo fastidioso» y el ser consciente de esto le ha hecho cambiar a un perfil de mejor colaborador. Es muy interesante la conclusión final sobre «lo estrechamente relacionado que está hacer una investigación rigurosa con divertirse en el proceso. Así que trato de hacer de la diversión una prioridad en mis proyectos«. Creo que esto puede ser un ejemplo muy útil para investigadores de todo tipo, para los que no es fácil encontrar colaboraciones duraderas. El rasgo más mejorable de muchas interacciones de colaboración es cómo es de agradable y divertida para los participantes, porque consciente o inconscientemente esto nos motiva a poner más o menos energía en la colaboración, y finalmente continuar o no con ella. El hacer un auto-análisis crítico, reconocer nuestros defectos, y proponerse mejorar, es una actitud muy valiente y acertada.

Building a healthy team through collaborative interactions

Versión en español de este artículo

This is just a quick note on a really big goal for researchers at any stage of their careers: having a good team around and being self-critical about it. I had an interesting conversation around this a short time ago. Searched and read a little about it and noticed something important that I’d like to continue thinking about.

An active researcher goes through a lot of critical collaborative interactions in their professional life. From bench work with colleagues, group meetings, training and coaching sessions, recruitment decisions as a principal investigator (PI), and so on. There is quite a lot of literature on management of teams, and also specifically about why attention to collaborations and building good teams are traits of a successful PI and lab (see references 1 and 2 listed at the end).

But having a remarkable scientific vision doesn’t mean one is good at recognising talent and attracting it. Even more, one might not be aware that one is not good at it at all. This is the idea I’d like to explore. That if this happens, we end up with teams that are fragile and inefficient, and the PI mainly responsible for it doesn’t fully realise it. The question then would be if there is anything that could lead a PI to be self-critical when they are not already, change their ways and improve their interactions. I don’t seem to have an answer for that. However, if a researcher indeed is able to self-reflect a bit about how their team and partner interactions work, we can plan a little on what can be done. I believe that it’s possible to quickly improve collaborative interactions by focusing on a few most important types of them and making a few practical shifts if needed:

> In group meetings, in coaching meetings, and other interactions where problems are discussed, reflect on how the team comes up with solutions. Train team members and yourself if you are the PI to avoid micromanaging others. Empower others to identify and realise solutions, even if they struggle and the results are not always perfect. Realise yourself that it takes time, training and experience to be a good coach and mentor.

> Also in meetings, reflect on how information is shared. As a rule of thumb, if information is shared only in one direction, the meeting could be very short or non-existent. Regular meetings to hear results and progress reports from colleagues or students are much more useful if are structured in a way that attendees can focus on discussion and decision making, with information shared in more than one direction.

> Research usually involves a lot of lab work, often using equipment and techniques that are not straightforward to students or researchers in other fields. A healthy lab culture, to follow good practices and to share experience, is usually a marker for team interactions. Make sure that you or someone in your team is able to train and help on the bench, side by side with your team members.

> Talk openly about career options. Think about having constructive conversations, what is needed for you or others to advance a career. Think long-term about who can help you and who are you willing to help. Develop meaningful relationships.

> Analyse your recruitment process. How candidates reach your door, how you conduct interviews. It is always a good idea to get help, second opinions from people that are good at recruiting good team members.

> Deal with team members and partners that you don’t want around you. In extreme situations when you conclude that personality traits make it impossible for someone to be in your team, learn your options.

References:

[1] Maria Olenick, Monica Flowers, Tatayana Maltseva, Ana Diez-Sampedro. Research in Academia: Creating and Maintaining High Performance Research Teams. Nursing Research and Practice, vol. 2019, Article ID 8423460, 3 pages, 2019. https://doi.org/10.1155/2019/8423460

[2] Ball, K., Crawford, D. How to grow a successful – and happy – research teamInt J Behav Nutr Phys Act 174 (2020). https://doi.org/10.1186/s12966-019-0907-1

Versión en español

Construyendo un equipo saludable a través de interacciones colaborativas

Esto son unas notas rápidas sobre un objetivo muy importante para los investigadores en cualquier etapa de sus carreras: tener un buen equipo y ser autocrítico al respecto. Hace poco tuve una conversación interesante sobre esto. Busqué y leí un poco al respecto y me fijé en algo importante en lo que me gustaría seguir pensando.

Un investigador activo pasa por muchas interacciones colaborativas críticas en su vida profesional. Desde trabajo de laboratorio con compañeros, reuniones de grupo, sesiones de formación y coaching, decisiones de contratación como investigador principal (IP), etc. Hay bastante literatura sobre la gestión de equipos, y también específicamente sobre por qué la atención a las colaboraciones y la creación de buenos equipos son rasgos de un IP y un laboratorio exitosos (ver por ejemplo las referencias 1 y 2 que se listan al final).

Pero tener una visión científica notable no significa que uno sea bueno para reconocer el talento y atraerlo. Más aún, es posible que uno no se dé cuenta de que no es bueno en absoluto. Esta es la idea que me gustaría explorar. Que si esto sucede, terminamos con equipos que son frágiles e ineficientes, y el IP principal responsable de esto no se da cuenta del todo. La pregunta entonces sería si hay algo que pueda llevar a un IP a ser autocrítico cuando aún no lo es, a cambiar sus formas y mejorar sus interacciones. Parece que no tengo una respuesta para esto. Sin embargo, si un investigador es capaz de reflexionar un poco sobre cómo funcionan las interacciones en su equipo y con sus socios, podemos planificar un poco lo que se puede hacer. Creo que es posible mejorar rápidamente las interacciones colaborativas centrándose en algunos de los tipos más importantes y haciendo algunos cambios prácticos si es necesario:

> En las reuniones de grupo, en las reuniones de coaching y en otras interacciones en las que se discute problemas, reflexiona sobre cómo el equipo llega a soluciones. Entrena a los miembros del equipo y a ti mismo si eres el IP para evitar la microgestión de otros. Empodera a otros para identificar y encontrar soluciones, incluso si tienen dificultades y los resultados no siempre son perfectos. Finalmente, hay que ser consciente de que se necesita tiempo, entrenamiento y experiencia para ser un buen supervisor y mentor.

> También en las reuniones, reflexiona sobre cómo se comparte la información. Como regla general, si la información se comparte solo en una dirección, la reunión debería ser muy corta o inexistente. Las reuniones habituales para escuchar resultados e informes de progreso de colegas o estudiantes son mucho más útiles si están estructuradas de manera que los asistentes puedan concentrarse en la discusión y la toma de decisiones, con información compartida en más de una dirección.

> La investigación suele implicar una gran cantidad de trabajo de laboratorio, a menudo utilizando equipos y técnicas que no son sencillas para los estudiantes o investigadores en otros campos. Una cultura de laboratorio saludable, de seguir buenas prácticas y compartir experiencias, suele ser un marcador para las interacciones del equipo. Asegúrate de que tú o alguien de tu equipo puede entrenar y ayudar en el laboratorio, junto a los miembros de tu equipo.

> Habla abiertamente sobre las opciones de carrera profesional. Piensa en tener conversaciones constructivas, lo que se necesita para que tú u otros avancen en su carrera. Piensa a largo plazo sobre quién puede ayudarte y a quién estás dispuesto a ayudar. Desarrolla relaciones significativas.

> Analiza tu proceso de contratación. Cómo los candidatos llegan a tu puerta, cómo realizas las entrevistas. Siempre es una buena idea obtener ayuda, segundas opiniones de personas que son buenas para reclutar buenos miembros de equipo.

> Decide qué hacer con miembros del equipo y socios que no quieres a tu alrededor. En situaciones extremas, cuando llegues a la conclusión de que determinados rasgos de personalidad hacen imposible que alguien esté en tu equipo, valora tus opciones.

Referencias:

[1] Maria Olenick, Monica Flowers, Tatayana Maltseva, Ana Diez-Sampedro. Research in Academia: Creating and Maintaining High Performance Research Teams. Nursing Research and Practice, vol. 2019, Article ID 8423460, 3 pages, 2019. https://doi.org/10.1155/2019/8423460

[2] Ball, K., Crawford, D. How to grow a successful – and happy – research teamInt J Behav Nutr Phys Act 174 (2020). https://doi.org/10.1186/s12966-019-0907-1

Management of (big) research teams

Ideas to successfully manage a team of researchers, and what success means in this context.

Some academic research teams are standalone, usually embedded in university departments, while others are part of research institutes or other structures with shared goals. Most teams are rather small, with an average (for biology research groups in the UK in 2015, from this reference) of around 7 members including staff and students. Published analyses of productivity of research groups suggest that small to medium-sized groups are more productive (same reference as above).

However, groups of much bigger size exist. There are not many, but groups of 30, 40 or even 100 members, with one PI, exist. They might have competitive advantages given the right conditions, or maybe they are just the unavoidable statistical outliers of an imperfect funding system. In any case, because of their oddness, learning about the way they are run could be useful for the smaller groups. The basic premise behind this post is that a big research group that has survived as such for some time is probably efficient and noteworthy in the way it’s managed. Their experience can be useful for smaller groups and also for created structures like research institutes that are in several ways similar to big research groups.

In connection with this, the existence of very big research groups puts to the test the definition of their productivity. In particular, it seems difficult to measure the role of PIs as teachers and mentors, and the contribution that post-docs and PhD students make in careers other than academic research. Additionally, one can often see examples that high productivity needs more than scientific talent, and group culture for instance has a big role in how productive a group is in the long term.

Recruitment. Really big research groups first grow to an intermediate size because of one or several PI that are able to gain funding while they employ talented people who are also successful in bringing in more funding, supervise students and so on. It is very important how the recruitment formal and informal processes in the group work, how the PIs and the group members are able to identify and attract other researchers with great talent and team building skills, and also with a same shared research vision. I believe this is the single most important factor for the creation of a big group, i.e. a PI with a remarkable scientific vision that also happens to be good at recognising talent and attracting it. This is also critical for small groups that while remaining small survive for a long time. Other pieces of the puzzle might be not in place but this is always quite outstanding in the big groups I have looked into.

In-house support. All big groups seem to have dedicated support staff other than the support staff at departments, schools, university services, that the group manages to hire and maintain over the years. Roles include project management, scientific coordination, laboratory management, help with proposal writing, financial administration, engagement/impact, business development, and others. It’s difficult to say how important this is for a group, but big groups that have been in place for a long time all seem to have a highly profesional support structure within the group that typically comprises of around 8-10% of the total group staff. Small sized groups might take advantage of this strategy as well, and should consider to budget support staff in their proposals.

Organisation of research activity. The group is formally or informally a combination of several research teams, each with one or several team leaders. The team leaders coordinate research activity within the team and ideally also manage the career aspirations, training needs, etc. of team members. One group in the UK asks all team members to «make a one page summary every two months describing their work, goals, collaborations, problems, & tasks with times. A key part of the OPS is that list the time left in the group, match this against their tasks, career aspirations».

  • Team meetings. Team meetings of one kind or another are usually weekly. Some groups have a meeting with the whole group weekly and another with only the smaller team also weekly. One to one meetings with the PIs or team leaders are also usually planned (every week, every two weeks, monthly… ) and sometimes upon request, although I think most groups / PIs in reality have only a guidance in place and usually attend requests as they happen.
  • Shared knowledge. Additionally to group meetings where ongoing work is presented and discussed, most groups usually have some kind of shared folders, group server or intranet, with different levels of access, where papers, awarded and rejected grants, raw laboratory data, presentations, meeting notes, and so on, are available. If managed properly this can be a great tool to teach students how to write, analyse and present information up to a high level.

Building group culture. A group, big or small, that openly discusses career aspirations of its members, that has a collaborative environment, that teaches not only the science but also how to be professional, transparent and accountable, has all the chances to be most productive in the long term. Its members are productive while in the group and also maintain productive links after leaving and moving to other groups, industry, or other fields altogether.

  • Solving disputes. Groups with a good culture have figured out how to best deal with problems between members. This depends on the PI personality and social skills, also on the team members’. The same protocols may not work well everywhere. In general, PIs that behave in a professional and transparent manner, that have empathy with personal life situations, that admit and correct mistakes, that do not let grudges or jealousy between team members unanswered, set an example that team members follow. Groups that for whatever reason don’t resolve disputes are bound to have mediocre collaboration within the group.

Collaboration with other research groups. Bigger groups seem to have more research partners in academia and in industry in particular. The ratio of partners per team member is probably not that different, but bigger groups have a critical mass that allows access to networking and industry leads that are more unusual to see in small groups.

Want more? There are many several good resources to learn about research group management. Here, to choose just one, I recommend a 1983 book ( High Output Management by Andy Grove) actually not focused at all at research groups, but at team management in general. This is an excellent book to learn about delegating, organising meetings, the critical importance of the team’s culture, leveraging one’s tasks to maximise the output of the team, monitoring/evaluating with indicators, and more. This summary is a good resource.

To finish. Should all teams follow the same best practice? Absolutely not. Your group might be better off doing some of these and some other different stuff. The aim of this post is to explain how most big groups do it and what could be useful to groups of all sizes.

In vitro models of tissues. Key industry players in 2021.

Clinical trials are the final step to demonstrate if a new drug is safe and works. Before testing on humans, drugs usually pass numerous preclinical studies. Sometimes a compound seems to be effective in its therapeutic function and non toxic in a preclinical model, but then it fails to perform equally in humans. Actually, it happens most of the time. Statistics from the year 2000 to 2015 [1] show that the average success rate for all drugs and vaccines is only 13.8%. Oncology has an astonishing low 3.4%. Companies spend a lot of money only to fail during clinical testing. And success in this case just means regulatory approval, not automatically that a drug works well, it may be because there aren’t any good alternatives. Trials fail for diverse reasons [2]. Most times failure can be associated with deceptive information from preclinical studies.

The collaboration between biologists and engineers, in academia and in industry, is blooming with new in vitro models of tissues and organs that can improve our understanding of disease and physiology [3-5]. These systems arise fundamentally from the availability of new protocols to produce induced pluripotent stem cells (iPSCs) and differentiate them into several cell types, and from a better understanding of cell culture in 3D microenvironments. They themselves provide new strategies to understand extracellular matrix (ECM) functions, cell-to-cell and cell-to-ECM interactions, matrix mechanical properties in each tissue or pathological condition, growth factor and other molecule-mediated mechanisms, that individually or together can lead to changes in cell phenotype and alterations in drug response.

Organoids and other in vitro models are showing that they can be excellent functional models of human disease and of normal non-pathological function. Research to produce humanised in vitro systems that more closely recapitulate native tissues has an immediate application in making drug discovery more efficient: it improves results of current in vitro methods to screen for drug candidates, allows testing high numbers of molecules, and generally avoids the use of animal models. A different application is personalised medicine and toxicology testing, where in vitro models of our own organs can be used to predict the effect of a particular drug on us, or identify which drug from a cocktail is causing an adverse event.

Recreating complex tissue functionality at microwell or chip scale still remains challenging, but there is a growing number of companies that develop and already offer advanced human in vitro models to support drug discovery and toxicology testing. This is a selection of market leaders and emerging stars (alphabetical order):

BioIVT (USA) – https://bioivt.com. Provider of biological samples including human and animal tissues, cell products, blood, and other biofluids. Services from target and biomarker validation, phenotypic assays to characterize novel therapeutics, to in vitro hepatic modeling solutions. Focus on  hepatic modeling solutions. Supplier of ADME-Tox model systems, including hepatocytes and subcellular fractions.

Biopredic International (France) – https://www.biopredic.com.  Initial focus on primary hepatocyte cryopreservation, now offers the isolation, production and distribution of fresh and frozen human and animal biological products, including tissues, primary cells, cell lines and reagents. Founded in 1993 from a technology transfer of INSERM.

Cellesce (UK) – https://cellesce.com. Bioprocessing technology for the expansion of human-derived cancer organoids. Founded in 2013 on expertise from the University of Bath and Cardiff University.

Crown Biosciences (USA / UK) – https://www.crownbio.com. Preclinical efficacy models, both in vitro and in vivo testing services, for drug discovery in oncology, inflammation, cardiovascular, and metabolic disease. Multinational company, part of JSR Life Sciences since 2018.

Cyprio (France) – https://www.cyprio.fr. Expertise in physicochemistry, liver biology and drug screening. With proprietary technology for the fabrication of spheroids. Created in 2017 at ESPCI Paris.

DefiniGENhttps://www.definigen.com. Human induced pluripotent stem cell (hiPSC) derived hepatocytes, intestinal organoids and pancreatic beta cells. Created in 2012 on results (OptiDIFF platform) from Ludovic Vallier lab at the Wellcome–MRC Cambridge Stem Cell Institute.

Emulate (USA) – https://www.emulatebio.com. Organ-on-a-chip technology for liver, kidney, intestine, brain, and lung. With multicellular configurations, primary cells, relevant ECM components, hardware and software apps. Created around 2013 as a spin-out of Donald E. Ingber lab at Wyss Institute / Harvard University.

Hubrecht Organoid Technology (Netherlands) – https://huborganoids.nl.  Organoids from epithelial tissue derived adult stem cells (ASCs) and related assay services, for applications in cystic fibrosis, cancer, immuno-oncology, toxicology, infectious diseases and other indications. Founded on results from Hans Clevers lab at the Hubrecht Institute for Developmental Biology and Stem Cell Research (KNAW).

Hµrel Corporation (USA) – https://hurelcorp.com. «Microlivers» from self-assembling co-cultures of primary cryopreserved hepatocytes with a non-parenchymal (stromal) cell line.

Kirkstall (UK) – https://www.kirkstall.com. Hardware platform to build organ-on-a-chip type of systems, based on interconnected cell culture flows. Established 2006 initially with technology from the University of Pisa.

Mimetas (Netherlands) – https://www.mimetas.com/en/home/. Microfluidic culture plates for organoids and spheroids. Related services and protocol for some in vitro models. Founded in 2013 in Leiden, the Netherlands. Now over 100 employees in four sites in the Netherlands, USA, and Japan.

STEMCELL Technologies (Canada) – https://www.stemcell.com/. Multinational biotech company with products and services for academic and industrial research, including reagents and culture media kits for the culture of organoids (kidney, intestinal, lung, forebrain) from human stem or primary cells. The intestinal organdie kit is developed with licences from Hubrecht Organoid Technology.

OcellO B.V.  (Netherlands) – https://ocello.nl. Cell line-derived spheroids and organoid models grown in matrix embedded 3D cultures. Patient derived xenografts, tumoroids, models, for oncology, immuno-oncology, inflammation and cystopathy indications. Drug discovery screening services. Created in 2011 on technology developed at Leiden University.

Qgel (Switzerland) – https://www.qgelbio.com. Patient derived organoids based on synthetic hydrogel technology. Cancer, colon, lung, breast, and pancreas applications. Founded in 2009 in Lausanne, Switzerland.

References:

[1] Chi Heem Wong, Kien Wei Siah, Andrew W Lo. «Estimation of clinical trial success rates and related parameters.» Biostatistics, Volume 20, Issue 2, April 2019, Pages 273–286. DOI: 10.1093/biostatistics/kxx069

[2] David B.Fogel. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemporary Clinical Trials Communications, Volume 11, September 2018, Pages 156-164. DOI: 10.1016/j.conctc.2018.08.001

[3] The promise of organoids and embryoids. Nat. Mater. 20, 121 (2021). DOI: https://doi.org/10.1038/s41563-021-00926-3

[4] Small. Special Issue: Advanced In Vitro Models for Replacement of Animal Experiments. Volume 17, Issue 15. April 2021

[5] Horejs, C. Organ chips, organoids and the animal testing conundrumNat Rev Mater(2021).

Where did all the nanomedicine go?

Well, it didn’t go anywhere, as in it didn’t disappear, but it seems that progressively for the last few years it has been not been calling itself «nano» that much anymore.

Last month we submitted a proposal to the last Euronanomed call, a ERA-NET type of platform under H2020 that has been running for twelve years and closes now. Also notably, the new Horizon Europe programme doesn’t have a NMBP programme anymore (NMBP stands for «Nanotechnologies, Advanced Materials, Biotechnology, and Advanced Manufacturing and Processing»), where many biomedical projects used to be funded. While naming things nano in science seems definitely less popular than 4-5 years ago, the area has probably grown in activity. The topics and the goals have merged with other areas where an application is envisaged (biomed, biotech, photonics, photovoltaics, etc.) and where the nanometer scale and the science behind it now uses more flowery and varied words to describe itself. The hypotheses here is that most niche research lines have evolved with their own vocabulary and keywords that include less the word nano. I figured out that it should be possible to trace and measure in some way how much the nanomedicine community continues to use it or not, and preliminary results are the matter of this post.

The European Commission has a public online database called CORDIS with information on all EU-funded R+D projects. For each project you can check for instance title, abstract, summary of periodic and final reports. I looked for projects in the H2020 downloadable spreadsheet that use the text string «nano» ( 1737 out of 19178 projects) on their summary or public periodical reports, and those that at the same time are related to biomedical and biotechnology applications (680 of the 1737). To simplify the search I looked for projects that contained several (4 or more) of a list of 20 words common in projects in those areas (health, clinical, pathology, cancer, bone, illness, biomaterial, biomed, injury, brain, stem cell, in vitro…). Then, to compare numbers year to year I normalised the number of projects related to nanomedicine by the total of projects in the year.

This was all done pretty quickly and improvement is very possible. I would like to refine how to identify projects within a chosen topic in the area, i.e. the list of words or by a different method, just not manually! Also it would be great to extend the series to include the FP7 database for 2007-2013 projects also available from CORDIS. A more ambitious goal that should be possible with these databases would be to identify which words and subtopics are getting more popular within the nanomedicine area, how their use moves over the years. Funded projects should be a good tool to predict emerging topics and interest, as they are one of the first public dissemination of ideas in science, many time earlier than publications for instance.

In the graph below we see number of EU-funded projects related to biomedical and biotechnology applications that use the text string «nano» per thousand projects of the total funded each year.

How to become a successful academic

Several times already during this last month I’ve seen myself in the troublesome situation of giving advice about how to become a successful academic. I’m not an academic myself so one idea that I try to gently put forward is that one way to be a successful academic is to move out. It’s an option to think about. The second idea that may help is that there are so many ways of being successful during a career at Academia. There are many because Academia doesn’t work in linear ways, although sometimes does, and many times contradicts itself. A few days ago I saw this Twitter thread by Maarten van Smeden (@MaartenvSmeden) that became very popular quickly because this is a topic of interest to many, and probably also because it’s a great sarcastic take on the available advice, highlighting how advice on how to become a successful academic can be so frustrating because it’s many times at odds with other advice, even from the same source. 

In his thread Maarten van Smeden offers lots of food for thought:
1) Be the ultimate collaborator but also don’t be. Say yes to as many collaborations as physically possible: co-produce papers, LEARN, co-write grants, DISCUSS, it is all about synergy. But also, collaborations slow you down, have your own ideas! Just say no to collaborations.
2) Be the methods ninja but also don’t be. Science is only as good as its weakest link: don’t be satisfied by applying the default analyses in the field. But also, don’t let perfect be the enemy of the good and don’t confuse reviewers. Just apply the default analyses in the field.
3) Be the superstar teacher but also don’t be. Professor means teacher, it is LITERALLY in the name. Being a good professor means being a superstar teacher. But also, focus on the science and minimize the hours of teaching, don’t try to become a superstar teacher.
4) Be the open science practitioner but also don’t be. A modern scientist is an open scientist. Open up your code, your data and your publications. But also, your code is messy, the data isn’t yours to share and you should save the APC of open publishing to hire new lab members.
5) Be the literature addict but also don’t be. READ YOUR LITERATURE. Be the literature addict and know what is out there to prioritize your own science and become THE EXPERT. But also, there is just too much! Invest time spend on reading in writing your own stuff! DON’T READ.
6) Be the supreme knowledge sponge but also don’t be. Become the best in the world by borrowing knowledge from different scientific disciplines and by working in multidisciplinary teams. But also, be THE SPECIALIST. Focus on your own discipline and team, your CV is begging you.
7) Be the social media rockstar but also don’t be. Outreach! Show you can and will communicate with the public to explain your science. But also, TIME DRAIN! Surely your tenure track committee is not impressed by your 30k SoMe followers half of whom are bots anyway.
8) Be the peer review soldier but also don’t be. Be an active part of the scientific community: be ready for peer review duty. The system will collapse without you! But also, peer review is a waste: everything will be published anyway. Don’t answer the calls for peer review duty.
9) Be the frequent flyer world traveler but also don’t be. You are an internationally oriented researcher: fly as much as you can for talks, collaborations and make sure you participate in ALL the discussions. But also, think about the environment: fly as little as you can.
10) Be the family person but also don’t be. Don’t forget to live while becoming successful: family time should always be the number 1 priority. But also, all of the above should be number 1 priority.