Labs naturally produce a lot of waste. For research purposes, materials need to be sterilized and can be costly to clean, and labs require more energy and utilities than traditional businesses — particularly in the biotech space.
At the same time, the costs to operate a biotech lab are increasing year over year, sometimes exceeding $100,000 per day. With supply chain disruptions, inflation, and a surge of demand for biotech research, biotechs are turning to more sustainable practices and reconfiguring their lab operations to reduce environmental impact and costs.
Here’s a look into how companies are building sustainable labs in life sciences, biotech, and R&D and developing supply chain resilience as a result.
Across all industries, environmental, social, and governance (ESG) initiatives are becoming more prominent as the climate crisis worsens and resources become more limited. Organizations who focus on ESG goals and sustainability are predicted to see more investments and better performance in 2022 and beyond.
In the lab, organizations like My Green Lab are working to “build a global culture of sustainability in science” by providing education and resources, and spreading awareness of sustainable lab practices in life sciences.
Research labs generate three to ten times more energy and water than traditional offices, and generate millions of tons of plastic waste each year, and labs that make an effort to be more sustainable can cut their energy use and costs by up to 40%, which has led to a recent push for more sustainability in the lab.
The “smart” and “green” labs of the future are more energy-efficient, automated, and conscious of waste and utility use, and make funding go farther by saving costs.
How can labs be more sustainable?
To start, labs should run an efficiency audit to identify areas of wasted supplies, manual work, energy use, and significant staff time. Then, lab managers should work closely with finance teams and bench scientists to automate workflows, incorporate technology, and increase efficiency — without forgetting to integrate tracking and data-collection into every step to keep an eye on sustainability metrics in the future.
To become more sustainable, lab managers or operators should:
As lab managers develop plans to make their labs more sustainable and reduce environmental impact, they can take small steps with big impact. For example, if you run an inventory audit and find hundreds of dollars worth of expired inventory, you can start by improving lab supply ordering, tracking, and management. When supplies are used and re-ordered efficiently, your lab saves money and reduces waste, all while helping experiments reach milestones more quickly.
Here’s what sustainable lab operations look like in biotech, R&D, and life sciences research.
1. Optimize lab spend.
Running a lean lab means that you aren’t spending unnecessary funding on things you don’t need, and that you are reducing overall waste (in time, supplies, and energy). Review lab spend on a monthly or quarterly basis to uncover areas that use the most budget. You may learn that expedited shipping fees, which put pressure on the already strained supply chain, are eating up significant costs. By developing a plan to pre-order bulk supplies, you’ll save time and money, all while reducing global energy consumption.
2. Develop supply chain resilience.
Supply chain resilience is the idea that labs have a strategic plan for supply ordering and management that is less impacted by fluctuating product availability and supply chain disruptions. To mitigate backordered products, take these steps to be proactive and strategic when ordering lab supplies.
3. Reduce biotech lab waste.
To reduce lab waste, you first need an understanding of what is waste, and what can be reused. Refer to My Green Lab’s guidelines on what lab materials can be reused and recycled, and explore take-back programs for certain lab items like styrofoam packaging.
Sustainability is more than just using recycled materials and turning off unnecessary lights at night. It’s a culture of being green in all aspects of lab operations, and it starts from when the company is founded.
Here are the biotech sustainability best practices labs should adopt to be more green.
Track all supplies from purchasing and shipping to ongoing inventory management.
Lab inventory and ordering management is critical to reducing waste. With efficient, digital processes, supplies are more likely to be used in a timely manner and experiments can run without fear of shipping delays. When all lab processes are streamlined, labs are more sustainable and use less waste.
Reuse, recycle, and return supplies, and train staff on sustainability.
Part of the Green Lab Program at UNC is training teams on being more environmentally conscious, so simple things like posters on equipment that notes whether it can be turned off when not in use or not can significantly reduce energy use.
Use energy-efficient lab equipment and high-quality supplies.
IoT (Internet of Things) connected devices like smart HVAC systems, lighting systems, and refrigerators and freezers can save labs thousands of dollars on utility bills, while making their labs more green.
Develop a culture of sustainability in the lab.
The Argonne National Lab’s Energy and Water Reinvestment Program earned them a 2021 U.S Department of Energy Sustainability Award — they completed 24 energy and water savings initiatives that saved over $180K in annual costs. Small measures add up, and when everyone in the lab, from finance to operations to bench staff, is invested, it makes an impact.
Looking to make your lab operations more efficient and sustainable? Explore our guide to going green in the lab.
When looking for ways to improve productivity in your lab, it is intuitive to look towards upgrading equipment or eliminating waste. While these are areas that can be strengthened, one area of waste that is often overlooked is wasting the talent you already have in house.
There is an easy way to capitalize on the most important resource in the lab: the personnel you already have. One easy solution to optimizing your lab without increasing any costs is through cross training. In simplest terms, cross training is the practice of training your scientists in a variety of different lab processes as opposed to having each scientist specialize in one area of the services you offer.
Staff who are trained on multiple processes, tests, and pieces of equipment will be flexible and able to jump in and support projects that are busy while other projects that require more specialized training are awaiting results or supplies. In a newly remote and flexible work culture, having more individuals who are comfortable supporting other projects will prevent delays in research and contribute to a more collaborative lab environment.
There are countless benefits of cross training from improved employee satisfaction and retention along with company cost-savings and efficiency.
The benefits of cross training come in the form of ease of scheduling for your lab managers, career development for your employees, and the ability to expedite processes that are more time intensive in a short window. While seemingly simple, the ability to maneuver through scheduling conflicts will help lab managers and employees alike. If a lab employee is suddenly sick or must take family leave, their desk will not pile up with work that no one else is able to perform. Employees will be much less stressed when they return from vacation or an emergency.
Cross training also contributes to career development for a handful of reasons. To start, you can get the most out of your staff while giving them a greater feeling of confidence in the entirety of various lab processes. This will attract the best candidates to your specific lab if they are aware of the amount of different knowledge they will gain, making them a more valuable employee in future career moves. Cross training creates cost-saving and time-saving opportunities as well — onboarding new employees can cost upwards of $30,000 per employee, whereas ongoing training is closer to $1,500.
Cross training has other benefits: when moving to a new space, you can complete a quick setup knowing that many employees can help set up many different processes. And importantly, you can get all hands on deck in crunch time. If you only have two employees able to do a given task, but need to hit a research milestone in a short time frame, the potential success is lost if your employees aren’t cross trained.
One of the easiest ways to begin cross training at any size lab is to analyze your processes from the business perspective. What lab services are the most sought after? Which are less popular? Is your allocation of lab staff proportional to these numbers? If some of your less popular lab services were to suddenly spike (like we have seen in the global pandemic, for example), would your staff be ready to all switch gears on a dime? These questions are a great place to realize the potential for cross training.
Once you identify areas of concern in terms of service coverage, you can begin to devise a plan of action in coordination with your most experienced lab staff. It is easy to think, “We are too busy to take the time to stop our work and spend time training staff that have been doing the same job for years.” In reality, the time it would take to cross train is well worth the efficiency and flexibility that will come as a result.
One thing to keep in mind when thinking about cross training is the actual process of identifying where your lab could benefit from this practice. Be strategic: carefully select staff and begin training every employee on every process. The first step to this is documenting and analyzing the ratio of processes and lab employees with abilities in each process. This could take some time to get accurate data if you’re not keeping it already.
When you begin cross training your staff as mentioned above, it is key to continue documenting who is completing the individual steps to maintain quality over the personnel completing newer tasks. This will help see who was making mistakes, why they are happening, and how you could improve on the next cross training session and all of those in the future. Keeping a good record of how quickly certain employees can pick up new skills will only make it easier the next time around.
Machine learning (ML) and artificial intelligence (AI) have become integral to many aspects of research and product development in biotech.
The various branches of the biotechnology industry ranging from human life sciences, food, agriculture, animal biotech, and industrial applications are leveraging the advancement of machine learning techniques to speed up research outcomes, cut operations costs, reduce manual efforts, and improve accuracy. Here’s how.
Implementing the laws of machine learning in agriculture can give a significant boost to food production, at a lower cost — increasing the GDP. According to Business Insider research, global spending on agricultural technologies — including machine learning — is projected to triple in revenue by 2025. Machine learning algorithms can be used in agriculture to educate farmers about best practices, identify crop ripening time, locate and remove weeds, and ensure better seed quality.
Uses for AI and machine learning in the health sector include drug screening, virtual health assessments, diagnosis, management and analysis of clinical trial data. This technology speeds up clinical trials and enables targeted therapies..
Today, cancer treatment teams are using AI and machine learning to design personalized cancer treatments, and better identify treatment options based on outcome predictions modeled using AI. Based on blood and bone marrow samples, biotech companies are using ML algorithms to build the most effective drug combination specific to the patient. The outcome analysis can predict effectiveness for individual patients, rather than give estimates based on samples of patients who may have different health backgrounds.
In the ongoing COVID-19 pandemic, scientists were able to save millions of lives by using machine learning and AI. These algorithms and programs helped scientists develop tests to screen for antigens, accelerate the identification of how to use the virus to generate an immune response, and discover and optimize new antivirals to treat COVID patients.
During the vaccine discovery process, scientists at Stanford used machine learning models called NetMHCpan and MARIA to identify “vulnerable spots on the virus”. This led the way to the development of vaccines that have protected millions from severe disease.
Thanks to technology, any person can now track and monitor their bodily processes using their smartphones and watches. More advanced ultrasound devices are also available that enable users to connect their smartphone and display the images from the machine in real-time.
With access to medical diagnostic devices that are powered by deep learning technology but can be used on the go, clinicians can bring the latest healthcare developments anywhere. And with cloud-based healthcare records, providers can send diagnoses and recommendations to teams of clinicians from anywhere in the world.
Lab managers are constantly looking for advanced ML algorithms to fuel fast-tracked innovation, production and development of drugs, chemicals, vaccines, and other biotech products. Here are a few ways machine learning accelerates biotech R&D.
Labs rely on knowledge management and cloud-based software to collect and transfer data. The information lab managers use and record day after day needs to be accurate and easy to come back to, or share later on for future discoveries.
With the power of technology, lab scientists have access to valuable data that can be standardized and turned into insights that help solve challenges and break down research barriers. Lab managers and scientists are maintaining massive databases that are further used by biotech labs and health organizations. This data is heavily reliable as it’s free of manual errors and accurate — and it plays a crucial role in identifying risk factors, expediting drug development, building personalized treatment plans, handling the supply chain, and analyzing huge amounts of data.
Calculating the permutations and combinations of various chemicals without having to perform the actual experiment accelerates the process of drug development and saves labs time and money. Scientists are also using ML programs that take over the manual tasks of data entry, analysis, and maintenance, adding back time that could be spent toward innovation.
If it hadn’t been for AI and ML, the vaccine for COVID-19 might not have come into existence. In addition to decoding data, ML software also helps scientists share the results of their studies with the scientific community across the globe. Many ML tools assist scientists in interpreting data, identifying patterns, and discovering solutions that couldn’t be seen before. Because of these ML advancements, discovery, accuracy and cohesion between biotech researchers has never been greater.
Data and intelligence are optimizing the biotech R&D process, and reducing the time to reach research milestones. In order to take advantage of the full potential of machine learning and AI, lab teams are focusing their efforts on identifying problems and creating tailored solutions.
As data becomes more accessible, organized, and universal, it frees up the minds of scientists to work on the process of testing hypotheses and bringing ideas to life. The rich, functional capabilities of technology combined with streamlined operational processes can produce advanced healthcare solutions. With more data comes more advanced medicine and better patient outcomes — just take a look at these trends in biopharma R&D in 2022.
In the lab of the future, robots conduct research, and AI and machine learning have made that a reality. Machine learning programs can take over data input and analysis, experiment modeling, evaluation, compound design, and synthesis, without requiring labs to spend time, resources, and budget on producing drugs that won’t end up being effective.
Source: Chemical & Engineering News
With robots and machine learning programs responsible for parts of the research process, biotech labs can process exponentially more data, with precision, and find meaningful research results, faster. Rather than fearing these technologies taking over the jobs of lab staff members, labs should embrace the possibilities of agile research and automating manual research tasks.
To learn more about optimizing the research process, read 6 Tips to Running an Efficient Lab.
Every year, millions of scientists publish research papers in biotech and life sciences journals. However, there is an industry-wide problem — the results aren’t easily reproducible. When scientists tried to reproduce their peers’ work, 70% of them failed.
In the United States alone, a study estimates that approximately $28 billion per year is spent on preclinical research that can not be reproduced. These numbers highlight the need for a better connection between academia, biotech labs, and the greater scientific community, and a need for more replicable experiments. Scientists also need to prioritize publishing research along with recommendations for others who may run the experiment in their own lab.
Along with a culture of showcasing research results in a good light, scientific research has been in a “reproducibility crisis” for many years. Scientists have “curated” their results, and the community has encouraged results over process. By celebrating failures, or results that may not be glamorous, and establishing an expectation of providing others with tools and resources to conduct their own research, the greater population will benefit.
Here are other ways that research can fail to be reproduced, limiting its potential to the greater scientific community.
For scientists to be able to reproduce academic studies, they must be able to access all the data and research material relevant (and often, also irrelevant) to the study. A lack of access to proper data hinders reproduction, and in the past, was understandable as collecting and storing data was challenging. Now, technology has made it easy for researchers to store and share unpublished data, research design, and discarded hypotheses so that it doesn’t impact experimental design.
A significant contributor to the non-reproducibility of scientific data is poor experimental design. Studies with undefined experimental parameters are not reported clearly and affect the ability to analytically replicate the data.
Cognitive bias refers to the ways that subjective thoughts and opinions impact decision making. Researchers strive for impartiality and avoid cognitive bias from impacting the research outcome, but it’s often difficult to eliminate it. Personal beliefs and perceptions can obstruct scientists from evaluating the data objectively and providing a bias-free template for reproducing the results.
To tackle the problem at its root, many scientific efforts have been directed towards conducting better research that can be translated into thorough experiments. Here are a few ways that more efficient experiment planning and design can impact research outcomes.
Following a standardized experiment planning process ensures uniformity across scientific research and brings innovation. Researchers following standard processes are more productive and save costs. Using a set of patterns while performing a scientific experiment allows them time to document their findings, data, deviating factors, and any other learnings that they find relevant. This optimized process allows them to function smoothly, safely and efficiently.
All the raw data from research studies need to be readily available for fellow researchers and scientists who aim to reproduce a study. Robust sharing of data, material, software, processes and other minute details reduce the likelihood of misinterpretation and significantly increase the probability of a scientific study coming to life.
To allow seamless sharing of data, some well-known journals have started providing additional space for expanded information. Making use of this space is voluntary and allows researchers to share more data if they’re willing to.
It’s important to include a thorough description of processes and research designs to improve reproducibility. Often, the ‘negative’ data that doesn’t support the hypotheses is discarded and not made a part of the final result. Surprisingly, this negative data can sometimes help to design experiments or interpret positive results from related studies. Therefore, researchers are encouraged to store all the data related to a study and share it with fellow researchers and scientists.
One of the most critical tasks in biotech research is to design experiments that can be reproduced. The National Institute of Health (NIH) now requires training in experimental design, and there have been efforts to improve the grant funding process to review experiments in more detail.
To meet industry standards and contribute to scientific progress, design experiments that are efficient, reproducible, and easily documented.
Document the aim of the experiment, the best method to achieve the goal, and the expected results. Remember to record all the information and expectations in the cloud so they are accessible to you at any time and don’t run the risk of being lost.
Documenting the protocol helps identify the reagents required for the experiment and the time taken to carry out the procedure. It’s best to draw up tentative deadlines and a buffer period, in case things go south. Create detailed timelines for each action along with responsible parties and any vendors or supplies involved. Cramming too much in one day may compromise efficiency and make your work more error-prone. Focus on one action at a time to ensure your research is reproducible in real time.
At the end of the experiment, record all your observations. Document your processes, tools, hypotheses, results, deviations from the protocol, difficulties you faced while carrying out the experiments, or any additional techniques that helped you get there. If the results deviate from your hypotheses, check back on the progress and try to identify where the study could have faltered — sometimes called a postmortem analysis. This information can prove valuable while troubleshooting the study and optimizing for future attempts.
It’s often noted that researchers only share with their peers the data and procedures they deem critical for their study to be reproduced. However, this process sometimes leads to the omission of minor pieces of information that may look otherwise, but are significant to the overall results.
Scientific reproduction is heavily dependent on the minutest of details that need to be recorded and shared with every researcher and scientist who has a stake in the process.
Recent developments in technology enable scientists to make data collection easy and error-free. Not only can this data be used to reproduce scientific studies, but is also significant to parallel or future research. The foundation of scientific experiments lies in the reproducibility of experiments. This is where all the stakeholders from across the biomedical industry must come together to refine strategies and develop solutions that bridge the gap between studies and experiments.
Getting this right will change the future of science. Read more: 5 Experiment Planning Lessons Scientists Learned from Navigating Through COVID-19.
The lab supply marketing is undergoing a shift, from the products required by scientists to the way that they purchase and interact with their favorite vendors. As the demand for novel therapeutics in response to the pandemic continues to rise alongside the automation of the lab supply industry, independent suppliers need to build brand awareness. More importantly, though, they need to understand the best ways to get their products in front of the right audiences.
Brand loyalty, which used to be a key aspect of biotech R&D and supplier-lab relationships, is no longer relevant during a supply chain shortage, prompting lab managers to seek out new supplies elsewhere.
As a result, this deprioritization of brand loyalty poses an opportunity for growth for smaller and emerging vendors. With customers caring more about the availability of supplies than the reputation of suppliers, the lab supply playing field evens out.
In order for suppliers to capitalize on the changing buying landscape for growing biotech organizations, suppliers need to use a collection of marketing strategies to engage their customers. Here are a few ways that lab supply vendors can connect with new audiences by improving their product marketing.
Innovation doesn’t stop for supply chain disruptions. In the scientific world, when a certain material, ingredient, or piece of equipment is out of stock, the need for that supply doesn’t just vanish. Instead, lab managers and procurement teams are left scrambling to find a suitable replacement.
For example, in the early months of the COVID-19 pandemic, there was a shortage of PPE (including gowns, gloves, masks, and safety goggles) at healthcare and research facilities globally. To combat the shortage in supplies from their usual vendors, procurement teams ordered smaller shipments from an assortment of brands. All of the supplies ended up being different colors and styles than they were accustomed to, but they met their safety needs so that they could continue working.
Brand loyalty isn’t just affecting the scientific community. It is diminishing across industries and even seeping into the lives of individual consumers. For example, when hand sanitizer was out of stock in 2020, people who were once Purell brand loyalists turned to other brands that had product available and that had a high enough alcohol content to be effective against the Coronavirus.
If there’s one thing that has been made abundantly clear over the past couple of years, it’s that the need for PPE, pipette tips, and other high-demand supplies has not waned. In order to continue serving their customers with the tools they need to get their jobs done, lab supply vendors need to increase their product availability and ramp up marketing.
Product marketing for lab supplies includes public-facing marketing efforts and more technical, detailed descriptions on product pages. Any product-related content can be strategic, and work to attract scientific buyers by using the right language and keywords.
Here are three steps that lab supply vendors can take to improve product visibility and awareness.
As the pandemic continues to affect the daily lives of the global workforce, lab supply vendors are realizing that their stock is pertinent to a wider audience than it was pre-pandemic.
In the scientific world, maybe lab procurement teams habitually purchased gloves from McKesson out of an obligation to brand loyalty. But, once those gloves were out of stock, procurement teams still needed to find product alternatives that met their needs and were available. Enter: smaller and emerging vendors without name recognition, but gloves in stock.
To reach an even wider audience, vendors can make product descriptions applicable to multiple industries. For example, outside of the scientific community gloves are still an essential supply for many professionals like cashiers, flight attendants, and bus drivers. By pivoting your marketing to include other industries, vendors gain access to a previously untapped demographic.
Increasing product availability requires vendors to seek out new customer terrain. Customers will be unable to purchase your supplies if they don’t even know you exist. By exploring new vertical markets, suppliers can ensure that they are exposing themselves to all potentially interested audiences.
Essentially, there’s no way of knowing if a certain market is right for your company until you are available to them. For example, if you previously served hospitals and healthcare facilities, you could expand to research facilities and scientific labs.
The final step toward complete product visibility is to ensure your supplies are as accessible as possible to a wide audience. For many small and emerging vendors, this step looks like joining a lab supply digital marketplace.
Digital marketplaces level the playing field for vendors of all shapes, sizes, and specialties by making it easy for vendors to be found. Customers are able to use marketplace search functions to shop by product availability and shipping preferences, which further removes the obstacle of brand loyalty.
Learn more about becoming a partner with ZAGENO.
The ultimate goal in pharmaceutical development is to go from the preclinical phase to clinical trials as efficiently as possible. Biotech co-founders and leadership teams are looking to keep up with competitors, while working on timelines that have been rewritten and accelerated by the COVID-19 vaccine breakthroughs.
Leadership teams also have to consider cost-efficiency, industry trends, the rising costs and unpredictable availability of lab supplies,and available technologies when planning out their drug development processes.
Fortunately, industry advancements are changing the way biotech enterprises research and develop drugs that save labs time and money, while improving safety and precision. Some of these techniques involve assessing and revamping current methods for patient recruitment, drug development, or site performance, making them cost-effective and more inclusive to wider patient populations.
Here are six ways that biotechs can speed up the drug development process using more efficient lab operations and R&D workflows.
Many large pharmaceutical companies continue to use clinical trial processes dating back to the 90s, and aren’t able to keep up with the emergence of new data sources and innovations. The data processing projects appear insurmountable, and new information comes in before existing research has been reviewed. The solution? Automating development stages of clinical trials.
Automation offers increased efficiency among team workflows, reduces the amount of hands-on time for repetitive tasks, and decreases the chance for human interference or error when handling data. It has the potential to enhance trial productivity in later stages, tighten up patient recruitment processes, and analyze data collected.
For biotechnology companies looking to implement automation, time is of the essence. Digital transformations in the lab can be a laborious undertaking that requires time, as well as a shift in the overall systems versus individual projects. It also requires more than a “set it and forget it” mentality — consistent checking and iteration of automated processes helps ensure systems are operating properly. Done right, though, the payoff is enormous — time spent now is time saved later, and more automated processes means more capacity for new projects.
Artificial intelligence (AI) in healthcare is on the rise, as many organizations look to adopt machine learning and other AI-driven methods. These efforts are aimed at reducing time spent researching and developing new drugs by eliminating the hours and funding typically dedicated to bench scientists manually processing data.
Traditional stages of early research, once taking four to six years to complete,now have shortened discovery timelines thanks in part to artificial intelligence. It also simplifies once arduous patient recruitment stages when researching new drugs.
In a 2019 study, researchers from the Research Center of the Sainte-Justine University Hospital, used advanced bioinformatic analysis to determine potential clinical responses in the early stages of drug discovery. This allows for researchers to predict potential drug implications on recruited patients, before starting clinical trials.
Pharmaceutical companies can audit existing site performances by utilizing available data sets, both internal and external. When data is plugged into predictive machine-learning algorithms, leadership teams are able to shift and realign company goals with existing sites to get back on track.
Alternatively, they can revamp existing sites using incoming or existing patient data. In the United Kingdom, the National Health Service surveyed patients’ conditions using a pre-established hotline, directing them to appropriate hospitals to treat said conditions.
Patient data collected from the phone line helped direct hospital leadership teams when working to establish future locations for clinical trial phases and research sites.
Even prior to the pandemic, digital health equipment was already quite prevalent within pharma tech, as a means of patient recruitment and participation in trials with low engagement.
Despite best attempts by the pharmaceutical industry to blend existing trial stages with new patient technologies, the pay-off is less effective than when implemented from the start. Enhancing patient trial experiences with equipment and tech, like e-consent and patient engagement apps, are best implemented in the beginning of a new trial process, rather than halfway through.
Additionally, biotech leaders can incorporate seamless operation strategies for lab staff throughout patient trials, using remote collaboration software. Employees are able to access confidential patient documents via secure cloud storage from both home and work, at any stage of the drug trial, preventing delays.
Taking advantage of cross-industry collaborations and partnerships, serves as another great starting point for reducing time frames during drug development.
Biopharma companies can improve the patient experience during clinical trials by taking a page from the consumer-facing world. With healthcare tech like apps that show lab results, the experience is smoother for both the lab and the patient.
Additionally, through collaborations with data professionals, like data scientists and machine learning engineers, biotech experts can improve their own data sets and storage processes, and develop techniques for enhanced analysis of existing drug data.
Co-founders should also consider joining biotech incubators and accelerators. Providing ample opportunity for lab staff and scientists to learn, collaborate and foster professional relationships, incubators are a fantastic resource for project development, and exploring future ventures within the biotech industry.
While not tech-driven per se, when biotech and pharmaceutical companies turn inwards to review their own workplace culture, and methods for analysis, this can greatly assist in reducing drug development timelines.
When leadership teams conduct thorough assessments of current workplace culture, potential opportunities for collaboration among team members emerge. This includes identifying and breaking down workplace silos for increased information sharing, fostering environments where iterations are encouraged, and facilitating employee growth and betterment.
Lab staff and researchers who have fulsome access to data are able to gain greater insight into the scope of each drug development stage, and perform tasks succinctly without unexpected disruptions. This includes remote employees. Over-communication of processes is crucial to ensuring tasks are conducted in an efficient manner, especially for remote work employees, as well as to improve cross-functionality of team members, and foster trust.
ZAGENO is the multi-vendor online marketplace for life science products. Learn more about how we simplify the procurement process and help biotech teams reclaim their scientific processes.
Complicating the experiment planning process slows down your lab staff’s ability to analyze data and deliver results. Archaic tools and equipment, delays from lab supply shipments, and inaccuracies in manual-record keeping all have the potential to halt the planning process, making future research and development increasingly difficult.
In the lab of the future, simplified methods and technology offering automation and accuracy are key to making the work and life of your lab staff that much easier. By streamlining the experiment planning and management process, labs have more time to focus on the experiment, and can decrease time to milestone. A side benefit is making research more reproducible and collaborative, supporting overall scientific innovation.
Researchers, lab managers, and leadership teams can set themselves up for success by learning the importance of using tech in experiment planning and the best strategies and tools to do so.
For biotech businesses early in their lifecycle, time and resources are of the essence. Planning experiments in advance, and using the right tech and equipment when doing so, creates efficient lab environments that run like well-oiled machines. Agile experiment planning also allows leadership teams to refocus their attention on company priorities, and value-rich resources like drug chemistry, and disease comprehension.
As you scale, planning becomes even more paramount, as lab teams grow larger in size, capital increases, experiments become more complex, and access to resources expands. Ginkgo Bioworks raised more than $900 million from investors, but it started as a team of five, founded on the ideals of transforming traditional biology into reusable code and sophisticated computing.
Using a broad range of technologies, including cloud-based collaborative software, electronic notebooks, and artificial intelligence keeps researchers informed about experiment planning stages and progress. It also helps biotech enterprises to streamline product and drug development.
As new technologies are developed and equipment is made widely available, new methods for experiment planning will emerge. Part of experiment planning also involves ensuring the technology and equipment researchers use is up to speed with the demands of your lab. Don’t lose out on valuable research and drug development opportunities because your methods for planning are stuck in 1983.
Below are four options to get you started with your own experiment planning in the lab of the future.
Notebooks are vital for accurate record-keeping, maintaining experiment results, noting material storage, as well as sharing discoveries with colleagues. However, one of the biggest challenges with traditional lab notebooks is keeping track of data spread out across various sources, and written by various authors.
An electronic notebook offers enhanced functionality, changing the way research is recorded and performed. In the beginning stages of experimentation, maintaining proper documentation allows for lab staff and researchers to easily repeat experiments. Electronic notebooks gather data, where disconnected spreadsheets and documents exist in silos.
Electronic notebooks also provide catalogs of where materials are located and how they’re stored, which is critical to lab efficiency. The difference between maintaining samples, and having to throw away compromised resources might be only a few degrees. Knowing which freezer, fridge, or cabinet your materials are stored in, as well as maintaining detailed records of temperatures within these spaces ensures sample integrity remains intact.
As experiment data becomes increasingly complex, not only within the planning stage but the analysis stage, artificial intelligence (AI) helps by automating, analyzing, and integrating vast amounts of information. AI also allows researchers to re-evaluate the design and planning of their experiments, while saving valuable time in the lab.
Unlike manual research capabilities, AI is able to operate at any time, and is not restricted by lab staff hours. In addition, machine learning is constantly adapting and learning about incoming data in order to make better decisions. For example, robotic applications working in tandem with AI are able to determine ideal reagent combinations for producing optimal reactions.
For startup biotech labs, initial analytics platforms may have less capabilities than those of a $100 million dollar pharmaceutical firm. Global pharmaceutical sales and marketing firm Eularis, developed E-VAI, a decision-making AI platform. Using machine learning algorithms, it produces analytical roadmaps, helping to identify future drivers for pharmaceutical sales.
Additionally, when first starting out, experimental planning might take place using ad hoc equipment and scraped together materials. With the example of Ginkgo Bioworks, the company purchased equipment well below market value when first starting out. Presently, their current automation labs cost over $500 million to maintain.
Eularis and Ginkgo Bioworks demonstrate potential growth that comes from planning and starting small. Their scaled-up technologies help biotech and pharmaceutical companies analyze existing data surrounding research and development.
Keeping tabs on incoming lab orders and supplies is just as important as detailing experimental discoveries. When valuable reagents for chemical analysis are out of stock, experimentation stages become dead in the water, and researchers fall behind on project timelines.
Part of agile experiment planning is selecting a life sciences marketplace that allows for efficient product research, timely shipping updates, and access to experts who can recommend appropriate product alternatives. It should also offer lab managers efficient tracking capabilities of delayed shipments, out of stock items, and scheduling updates.
Life science marketplaces, like ZAGENO, offer diversified lab supplier options from thousands of vendors, ensuring multiple sources to source supplies from. Even if one supplier is delayed, lab staff can proceed with other experimental components, knowing their remaining supplies are enroute.
Diversifying your current tech and equipment with new tools and methods will help your lab team streamline experiment processes, ultimately leading to more efficient lab processes overall. Knowing how, and when to invest in setting up or revamping your tech stack will create long-term success for lab teams.
Learn more about simplifying the procurement process for biotech companies and take an extra step towards turning your lab into the lab of the future.
Biotechnology and medicine are closely linked, both studying the human body and trying to eradicate disease, and improve human health. Innovations in both fields impact one another, with crossovers like the discovery of synthetic insulin and gene sequencing advancements.
During the COVID-19 pandemic, there have been even more biotechnology needs in the medical field, with biotech-focused vaccine research and mRNA developments playing a major role in public health and safety.
With public attention on biotech research, funding and recent breakthroughs have already led to advancements that will change healthcare and disease prevention. For example, in January 2022, researchers at the University of Pennsylvania found that a vaccine-like mRNA injection can create CAR T cells without having to harvest and reprogram the cells first. This could change cancer treatment and lead to new advancements in treating other autoimmune conditions as a result.
Let’s explore some recent biotech research projects with a focus on human health, pharmaceutical development, and healthcare practice.
Juno Therapeutics, an early leader in cell and gene therapy, inspired the growth of several related biotechs in the past decade since their founding. Juno alums went on to found related cell therapy research companies including Affini-T Therapeutics, Umoja, and GentiBio.
HotSpot Therapeutics recently raised $100M to develop new therapies using AI-driven technology to identify and target proteins in the body that contribute to disease. They are developing new therapies that will offer targeted treatments for autoimmune diseases, cancers, and other illnesses that have never been used before. HotSpot will begin clinical trials of a new lung cancer treatment in 2022.
In February 2022, Thermo Fisher and Medidata Acorn AI announced their partnership to transform clinical trials in life sciences through a new AI-based platform — PPD TrueCast. This new platform will provide pharmaceutical and biotech companies with predictive modeling and detailed analytics to improve clinical research site selection, increase patient enrollment in clinical trials, and reduce the length of trial cycles. Using machine-learning insights and AI, and with information from over 25,000 clinical trials and almost 8 million patients, the system is predicted to improve the accuracy of clinical trial milestones by 30%.
Viome Life Sciences has raised over $100M to develop their early intervention diagnostics and therapeutics for aggressive cancers. Viome has partnered with GSK, and they secured FDA designation for their platform that screens individuals for early-stage oral and throat cancers. With a new 25,000 square foot lab facility in Washington, this team of researchers has a promising opportunity to make major strides in healthcare and diagnostics.
Health diagnostics are a rapidly growing field of biotech research and development. In 2020 alone, health diagnostic startups raised almost $5B in funding. Curative, an on-demand testing biotech startup, pivoted during the COVID-19 pandemic to provide on-demand COVID testing for communities via drive-thru, walk-up, and mobile sites.
mRNA vaccines had been in development since the 1990s, and during the COVID-19 pandemic, mRNA rose to the forefront of hundreds of biotech research teams’ queues. Researchers at Moderna, BioNTech, Pfizer, Johnson & Johnson, and other biopharma companies rushed to implement research that had been in the works for decades.
Now, new research is showing that a vaccine-adjacent injection using mRNA technology can make CAR T cells in the body, showing hope for cancer treatment.
The biotech healthcare market is only going to grow more as both science and technology advance. AI and machine learning will make diagnostics more customizable and patient-specific, helping to make medicine more equitable and effective for patients. As companies develop new technologies and ways to conduct research, it’s up to the scientific community to share ideas and collaborate in the spirit of innovation.
Technology makes biotech research more accurate, accessible, and data-driven, as well as more shareable with the community. Learn how biotech labs have undergone digital transformations, from procurement to experiment planning.
Following two years of massive healthcare advancements related to vaccine developments, biotech has exploded with opportunities for new research that impacts medicine.
And along with these new developments, there have been recent changes to ICD-11, the International Classification of Diseases from the WHO that define aging as a disease — opening the door for additional research and funding in lifesaving treatments.
With advancements in medicine fueled by developments in biotechnology come new investments and breakthroughs that impact the entire healthcare and pharmaceutical industry. More awareness and funding opens the door for new preventative care and treatment of diseases like cancers, autoimmune disorders, and other conditions.
There has been some debate around classifying aging as a disease, rather than as a certain outcome of living longer. Scientists like David Sinclair have been researching the cellular lifespan as it relates to aging and teams have made recent discoveries that indicate the possibility of regenerating organs, improving cognitive function in old age, and overall slowing the epigenetic clock.
Funding for anti-aging or longevity research in biotech is rising, with anti-aging biotech company Life Biosciences recently procuring an $82M round of funding for R&D of therapeutic treatments for aging-related diseases. Another aging research biotech lab, Altos Labs, also secured quite a bit of financing, with $3B in initial investments from high-profile backers including Jeff Bezos and Yuri Milner. Altos Labs seeks to create epigenetic-based anti-aging technology.
David SInclair's Biotech Research Lab at Harvard
David Sinclair and colleagues have been tracing aging back to its roots for decades, trying to find the source of aging, which they believe to be the contributing factor to diseases like cancer, dementia, and other age-related conditions. After the Nobel-prize winning discovery of Yamanaka factors in 2012, labs around the world, like David Sinclair’s, have been making discoveries that show promise for longevity treatments in the near future.
The Sinclair Lab at Harvard is a biology lab currently researching the body’s response to and defenses against aging to understand how to treat diseases including mitochondrial diseases, diabetes, Alzheimer’s, cancers, and other heart diseases
Biotech research is more intelligent now than it has ever been before. Biotechs are using AI and statistical modeling to repeat experiments and collect data, and with wearable sensor technology, they can collect data from human subjects at scale in completely new ways.
When running a biotech lab in 2022, lab managers and PIs are turning to cloud-based collaborative technologies, new sources of funding, and more efficient lab operations to make research progress amidst supply chain challenges and inflation concerns.
Many labs are making strides in healthcare and medicine-related therapies all around the globe.
Genflow Biosciences is studying the sirtuin 6 (SIRT6) gene, an age-related gene, and has developed therapies to help treat conditions like idiopathic pulmonary fibrosis and Werner’s syndrome.
Chen, Zeng, et al (2022) published research that showed that Resveratrol, a plant compound and naturally occurring antioxidant, helped to improve myocardial fibrosis through genetic regulation. This promising study on rats with dilated cardiomyopathy indicates a need for further research on this medication.
In 2021, Tavakoli, Pour-Aboughadareh, et al, published a paper, “Applications of CRISPR-Cas9 as an Advanced Genome Editing System in Life Sciences”. Their research detailed how CRISPR gene editing technology can be applied to healthcare, showing examples of clinical trials that showed patient responses to treatment, like no longer requiring blood transfusions, a development that could impact cancer patients and those with blood disorders greatly.
Biotech companies were previously focused on one biological pathway, like a specific genetic pathway, or gene, or studying the ways that a specific protein would impact humans. Biotechs are to thank for advancements in medicine like synthetic insulin. Now, biotechs can take new therapies all the way from R&D through clinical trials and to market.
McKinsey notes that biotechs like BridgeBio and Roivant Sciences now operate using portfolios, spinning up new branches of their organizations to research and develop individual drugs and therapies. Having multiple drugs in development at once helps biotech companies to stay in business, independent of one drug of 20 failing to make it to market.
Companies working on biotech research and pharmaceutical R&D have quickly had to adapt to smart business models, adopting strategic experiment planning and finance management to make the most of research dollars. Through research partnerships and development of both hyper-specific and more general biotech concepts, the entire industry benefits, resulting in innovative products and technologies that can be used and expanded by other labs, or adapted for different use cases.
Biotech healthcare products include:
Advancements in biotechnology and healthcare research have significant implications for medicine and improving quality of life for all. Learn more about the Hot Research Topics in Biotech in 2022 and see where investments are going to pay off in healthcare biotech research.