AI in Pharmaceutical Research: How Machine Learning Accelerates Drug Discovery and Development

AI in Pharmaceutical Research: How Machine Learning Accelerates Drug Discovery and Development

by Ree AI Health Tech Med Tech

AI in pharmaceutical research is booming. Artificial intelligence (AI) and machine learning (ML) analyze enormous volumes of clinical and biological data with amazing speed and accuracy, allowing them to generate and evaluate a wide range of medication formulation options. Let’s learn more about how they do it.

Contents

ML and AI-driven applications in pharma: from research to discovery

Generative AI can help address complex formulation challenges and develop personalized medicines (UsefulBI, 2024). Combined with ML, AI also brings new opportunities for disease diagnosis, medical imaging, treatment personalization, drug safety monitoring, drug repurposing, and big data analysis to make better decisions (Vamathevan, J., et al., 2019).

ML techniques like supervised learning, and reinforcement learning, and their applications can help facilitate pharmaceutical operations (Wadighare and Deshmukh, 2024). These applications include:

  • Drug discovery and design
  • Research and development (R&D)
  • Disease prevention and diagnosis
  • Epidemic prediction
  • Email detection
  • Speech recognition
  • Data mining

Large-scale data analysis is the foundation of these applications. Next, we’ll explore how big data analytics is transforming drug development.

Big data analytics in drug development turn information into insights

The explosion of biological and clinical data such as genomics, imaging, and the use of digital wearable devices has created both opportunities and challenges for drug developers. ML techniques are invaluable to glean meaningful insights from this deluge of information, informing decision-making at every stage of the drug development process (Topol, 2019).

Recursion is a company leveraging big data analytics in a way never seen before. Conducting over 2 million experiments per week, they generate and store 20 to 25 petabytes of data on their in-house supercomputer, Biohive-1. They’ve also partnered with NVIDIA to use its DGX Cloud supercomputing power, allowing them to predict the targets of 36 billion molecules (Brazil, 2024).

Such methods also offer benefits after market research with the use of “big data” from real-world data sources. These sources can enrich the understanding of a drug’s benefit-risk profile, better understand treatment sequence patterns, and identify subgroups of patients who may benefit more from one treatment compared with others, or precision medicine (Schneider, 2018).

Close up of shelves with medication

Smarter medicines: How AI can optimize drug formulations

AI can create more stable and effective medications with improved drug delivery systems. According to UsefulBI, Yang, and Topol, AI can also:

  • Predict drug properties.
  • Optimize dosage forms. 
  • Detect potential drug interactions, providing warnings to healthcare professionals to prevent harmful combinations of medications.
  • Suggest novel excipients, particularly useful in addressing complex formulation challenges and developing personalized medicines.

These capabilities are especially valuable in developing new formulations that optimize for specific characteristics such as stability, bioavailability, or controlled release profiles (UsefulBI, 2024). 

The integration of generative models in de novo drug design is of particular interest. These models can create entirely new molecular structures that are optimized for specific properties, potentially leading to the discovery of novel chemical entities with superior drug-like characteristics. 

Epidemic prediction

One significant application is in epidemic prediction. Pharmaceutical companies and healthcare industries are using ML and AI technologies to monitor and verify the spread of infections worldwide. These modern technologies consume data from various sources, analyzing environmental, biological, and geographical factors affecting population health in different geographical areas. This approach helps predict and even mitigate the impact of future epidemics (Bullock et al., 2020). 

Man and woman working in a lab with flasks

Pharmacovigilance (drug safety)

In the field of pharmacovigilance, AI and ML algorithms can help pharmaceutical companies and regulatory agencies identify potential safety issues with medications more quickly. This capability is crucial for ensuring patient safety and refining drug formulations (Bate et al., 2018). 

Moreover, AI is being used to optimize drug formulations, creating more stable and effective medications with improved drug delivery systems. It can also detect potential drug interactions, providing warnings to healthcare professionals to prevent harmful combinations of medications (Yang et al., 2019).

Supply chain and manufacturing optimization

Beyond research and development, ML is also making significant contributions to supply chain and manufacturing optimization in the pharmaceutical industry. It’s being used to predict demand, optimize inventory levels, and improve quality control in manufacturing processes. In drug marketing and sales, ML algorithms can analyze market trends, predict drug performance, and optimize marketing strategies (Ramanathan, 2023). 

One of the most crucial applications of AI in drug discovery is target identification.

Target identification powered by AI and ML 

Illustration of 3 people in a lab

One of the most crucial and time-consuming steps in drug discovery is identifying viable therapeutic targets. Traditionally, this process could take years of painstaking research. However, AI-powered target identification is dramatically accelerating this phase, allowing researchers to sift through enormous amounts of biological data with unprecedented speed and accuracy (Schneider, 2018). 

AI is widely used for multi-target drug innovation and biomarker identification, offering efficiency and accuracy that were previously unattainable. Pharmaceutical companies are using AI-powered tools and ML algorithms to streamline drug research, development, and innovation processes around the world (Wadighare and Deshmukh, 2024).

ML algorithms can analyze complex datasets like genomic, proteomic, and clinical data, to identify and study disease patterns, and determine which composite formulations are best suited for treating specific symptoms of particular diseases. These AI systems can detect patterns and relationships that might be overlooked by human researchers, to discover novel targets and pathways (Ching et al., 2018). 

ML is also being used to predict protein structures, design new molecules, and simulate drug-target interactions, significantly speeding up the drug discovery process (Ramanathan, 2023). These approaches not only accelerate the drug discovery process, but also have the potential to address rare diseases more effectively. 

Examples

Companies like Benevolent AI are at the forefront of this revolution. Their platform connects structured data from clinical and chemical databases with unstructured data from scientific literature, creating what they call “an enormous hairball of interconnected facts.” This approach allowed them to identify PDE10 as a novel target for ulcerative colitis, a connection not explicitly stated in existing literature (Brazil, 2024).

Another notable success story in AI-driven target identification comes from Insilico Medicine, whose AI platform helps them predict the best formulations, reducing the need for trial-and-error experimentation and accelerating the development process (UsefulBI, 2024). Insilico’s AI-generated anti-fibrotic drug became the first of its kind to reach Phase 2 clinical trials. This milestone demonstrates the potential of AI to not only identify targets but also to guide the entire drug discovery process from conception to clinical testing (Insilico Medicine, 2024).

While identifying targets is crucial, predicting the properties of potential drug candidates is equally important. That’s where deep learning comes into play.

Deep learning for molecular property prediction

AI image of a colorful molecular compound

Deep learning has revolutionized the field of molecular property prediction, enabling researchers to assess the potential of drug candidates with remarkable accuracy. This technology is particularly valuable in predicting Absorption, Distribution, Metabolism, and Excretion (ADME) properties and toxicity, crucial factors in determining a drug’s viability (Yang et al., 2019).

Compared to traditional Quantitative Structure-Activity Relationship (QSAR) methods, modern deep learning approaches offer several advantages. They can handle larger and more diverse datasets, capture non-linear relationships more effectively, and often require less manual feature engineering (Gao, et al., 2020). For instance, graph neural networks have shown exceptional performance in predicting molecular properties by directly learning from the structural representation of molecules (Wu et al., 2018).

Real-world applications of deep learning in property prediction are already yielding impressive results. Pharmaceutical companies are using these models to screen huge libraries of compounds, significantly reducing the time and cost associated with early-stage drug discovery (Zhavoronkov et al. 2019). For example, deep learning models have been successfully employed to predict drug-induced liver injury, a major cause of drug attrition in clinical trials (Xu et al., 2015)

However, it’s important to note that while deep learning models excel at pattern recognition, they may struggle with extrapolation to novel chemical spaces. Researchers are addressing this limitation by developing more robust models and incorporating techniques like transfer learning and multi-task learning to improve generalization (Goh et al., 2017).

Predictive modeling

Man and woman working in a lab wearing masks

In the pre-clinical space, natural language processing (NLP) is being used to extract scientific insights from biomedical literature, unstructured electronic medical records (EMR), and insurance claims to ultimately help identify novel targets. 

Predictive modeling is another area where ML is making significant strides in clinical trial design. Predictive modeling can predict protein structures and facilitate molecular compound design and optimization, enabling the selection of drug candidates with a higher probability of success (Ching et al., 2018). In addition, ML plays a crucial role in genomics and proteomics research, helping to identify genetic markers associated with diseases and potential drug targets (Ramanathan, 2023). 

By analyzing historical trial data and incorporating real-world evidence, these models can forecast potential outcomes and identify potential pitfalls before a trial begins. This foresight allows researchers to optimize trial protocols and resource allocation, potentially saving millions of dollars and years of development time (Gayvert, 2016).

Despite these promising applications, the use of AI in clinical trials raises important ethical considerations and regulatory challenges. Ensuring patient privacy, addressing potential biases in AI algorithms, and maintaining transparency in decision-making processes are crucial concerns that the industry must navigate. Regulatory bodies like the FDA are working on developing guidelines for the use of AI in drug discovery and clinical trials to address these issues (FDA, 2023).

With promising drug candidates identified, the next challenge lies in designing effective clinical trials to test these compounds.

Clinical trial design optimization

Group of researchers in a clinical trial

In the realm of clinical data assessments, AI and ML are revolutionizing how healthcare data is analyzed and utilized. These technologies are being applied in various areas, including disease diagnosis, medical imaging analysis, treatment personalization, and clinical trial optimization (Alam et al., 2023). 

The application of ML in clinical trial design is transforming how pharmaceutical companies approach this critical phase of drug development.

ML applications in clinical trial design

ML is transforming clinical trial optimization to improve patient recruitment, predict patient dropout rates, and optimize trial design. AI-driven patient selection and stratification are enabling more targeted and effective trials, potentially reducing the high failure rates that have long plagued the pharmaceutical industry.

Advanced techniques like Bayesian nonparametric learning are emerging as powerful tools in clinical trial design and analysis. These methods allow flexible shrinkage modeling for heterogeneity between individual subgroups and automatically capture additional clustering, requiring fewer assumptions than more traditional methods (Kolluri et al., 2022). 

ML algorithms can analyze patient data such as genetic information, medical history, and lifestyle factors, to identify the most suitable candidates for a trial. This precision approach not only increases the likelihood of trial success but also helps in developing more personalized treatments (Woo, 2019).

AI applications in clinical trial design

AI-driven patient selection and stratification enable more targeted and effective trials, potentially reducing the high failure rates that have long plagued the industry (Harrer et al., 2019).

Big pharmaceutical companies are leveraging AI for clinical trial design as well. For example, GSK developed its own in-house large language model (LLM) called Jules OS, capable of autonomously performing tasks and responding directly to staff questions. The company has used AI “right across the value chain” since 2019, including in clinical trial design for drugs like bepirovirsen, their investigational treatment for chronic hepatitis B (Bender & Cortés-Ciriano, 2021).

However, it’s crucial to strike a balance between computational predictions and experimental validation. While AI can significantly narrow down the search space and suggest promising drug candidates, the complexity of biological systems means that experimental testing remains essential. Researchers are developing iterative approaches that combine AI predictions with rapid experimental feedback to optimize this process. 

AI and ML are already making significant impacts across the pharmaceutical industry. But what does the future hold for these technologies?

The future of AI and ML in pharma

Pharmacists in lab smiling

AI is revolutionizing drug discovery from target identification to clinical trial design, offering unprecedented speed and efficiency. Companies like Benevolent AI, Insilico Medicine, Recursion, GSK, and Lantern Pharma are using AI to identify novel drug targets, design molecules, and optimize clinical trials. 

While AI shows great potential to reduce drug development time and costs, several challenges remain. The quality and diversity of input data significantly impact the accuracy of AI predictions. Validating AI-identified targets and formulations through experimental methods is crucial, as computational models may not capture all the complexities of biological systems (Vamathevan, 2019). Other challenges include: 

  • Data preparation and integration
  • Intellectual property concerns
  • Lack of skilled personnel with domain-specific knowledge
  • Quality and representativeness of training data
  • AI tool integration with existing pharmaceutical workflows
  • Regulatory considerations for AI-assisted formulation development

Researchers are working to address these limitations by improving data integration techniques and developing more sophisticated AI algorithms that can better handle the intricacies of biological networks (Schneider, 2018).

The integration of AI and ML in pharmaceutical research is not just about replicating human capabilities; it’s about identifying principles that allow agents to act intelligently and improve upon human capabilities. However, not every research question can be answered with AI and ML, particularly if there is high variability, limited data, poor quality of data collection, under-represented patient populations, or flawed trial design (Topol, 2019).

Despite the challenges, generative AI is poised to significantly impact pharmaceutical formulation, leading to more effective and tailored drug products. In the future, the combination of ML (particularly deep learning), with AI, human expertise and experience is likely the best approach to coordinate and analyze the huge and diverse data stores in pharmaceutical research and development (Alam et al., 2023). 

ML and AI are not just buzzwords for the pharmaceutical industry–they’re powerful tools reshaping the entire process of drug discovery and development. From identifying new targets to optimizing lead compounds, AI is accelerating research, which can bring life-saving treatments to patients faster than ever before. While challenges remain, the future of drug discovery looks bright with ML and AI at the helm. 

References

Alam, M. S., et al. (2023). Applications of Artificial Intelligence and Machine Learning in Pharmaceutical Research. GSC Biological and Pharmaceutical Sciences, 24(1), 001-009. 

Bate, A., et al. (2018). Artificial Intelligence in pharmacovigilance: Using machine learning to detect duplicate adverse event reports. Drug Safety, 41(6), 591-597.

Bender, A., & Cortés-Ciriano, I. (2021). Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discovery Today, 26(2), 511-524.

Brazil, Rachel (2024). How AI is transforming drug discovery. The Pharmaceutical Journal,  2024.313(7989) doi::10.1211/PJ.2024.1.322137 

Bullock, J., et al. (2020). Mapping the landscape of artificial intelligence applications against COVID-19. Journal of Artificial Intelligence Research, 69, 807-845.

Ching, T., et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387.

FDA. (2023). Artificial Intelligence and Machine Learning in Software as a Medical Device.

Gao, K., et al. (2020). Interpretable drug target prediction using deep neural representation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1396-1405).

Gayvert, K. M., et al. (2016). A computational approach for identifying synergistic drug combinations. PLoS Computational Biology, 12(1), e1004756.

Goh, G. B., et al. (2017). Deep learning for computational chemistry. Journal of Computational Chemistry, 38(16), 1291-1307.

Harrer, S., et al. (2019). Artificial Intelligence for Clinical Trial Design. Trends in Pharmacological Sciences, 40(8), 577-591.

Insilico Medicine. (2024). Press Release: Insilico’s AI-generated drug enters Phase 2 clinical trials.

Kolluri, S., et al. (2022). Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review. AAPS J 24(1), 19. doi:10.1208/s12248-021-00644-3.

Moraffah, B. (2024). Bayesian Nonparametrics: An Alternative to Deep Learning. ArXiv, https://arxiv.org/html/2404.00085v1 (accessed 8 July 2024).

Ramanathan, V. (2023). Machine Learning in the Pharma Industry. Linkedin Pulse, https://www.linkedin.com/pulse/machine-learning-pharma-industry-venugopal-ramanathan (accessed 7 July 2024). 

Schneider, G. (2018). Automating drug discovery. Nature Reviews Drug Discovery, 17(2), 97-113.

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

UsefulBI Corporation. (2024). Optimizing Drug Formulation: Generative AI’s Role in Enhancing Pharmaceutical Product Development. Linkedin Pulse, https://www.linkedin.com/pulse/optimizing-drug-formulation-generative-ais-role-enhancing-3js7c (accessed 7 July 2024). 

Vamathevan, J., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463-477.

Wadighare, U.A., & Deshmukh, S. P. (2024). A review on artificial intelligence and machine learning used in pharmaceutical research. GSC Biological and Pharmaceutical Sciences, 26(01), 191-198.

Woo, M. (2019). An AI boost for clinical trials. Nature, 573(7775), S100-S102.

Wu, Z., et al. (2018). MoleculeNet: a benchmark for molecular machine learning. Chemical Science, 9(2), 513-530.

Xu, Y., et al. (2015). Deep learning for drug-induced liver injury. Journal of Chemical Information and Modeling, 55(10), 2085-2093.

Yang, X., et al. (2019). Concepts of artificial intelligence for computer-assisted drug discovery. Chemical Reviews, 119(18), 10520-10594.

Zhavoronkov, A., et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038-1040.

Zhu, H. (2020). Big data and artificial intelligence modeling for drug discovery. Annual Review of Pharmacology and Toxicology, 60, 573-589.

Working Remotely for Almost 20 Years: Why I’ll Never Go Back to the Office

by Ree career
Credit: Neo Latrica for CreateHerStock

What’s more important at work: being physically present, or being productive? I think it’s the latter, but thousands of corporations are now (in 2022) setting their mandates about returning to the office.

Since I’ve been working remotely since 2005, I’m a pro at it, and I love it—I have NO plans to ever work in a corporate office again. This article shares my thoughts about remote work, and findings in the latest HubSpot Hybrid Work Report as a Black woman working remotely in tech for my entire career, both as an employee and an entrepreneur.

Remote-Working OG? That’s me!

I started working from home (WFH) as a “virtual employee” in 2005–that’s how I was referred to within the company. Back then, it was widely referred to as telecommuting instead of remote work, but it’s the same thing. So when the whole world started working from home, I didn’t have to adjust. However, it DID help me leave a job I held for 22 years to move on to greener pastures.

I was the envy of everyone I knew, because not only did I get to work from my house, my employer at the time also allowed me to get reimbursed for my internet and phone expenses, since they were required for my home office. (They were already saving money by letting go of the real estate that held all of our cubicles.)

One of the reasons I stayed with that employer for so long was that I was 100% remote. Somehow, I survived multiple mergers and acquisitions in those 22 years. It’s a known fact that being visible, that is, physically present in an office (”presenteeism”), often weighs in on promotions and upward mobility, regardless of the individual’s contributions and productivity. But thankfully, I’ve never had that problem—one of my promotions happened during a time of layoffs, simply because I kept track of my accomplishments and was vocal about it.

Purchased from Styled Stock Society

WFH opened doors wider for freelancing and entrepreneurship

Over my 23-year writing career, I’ve also become a speaker, an author, a life coach, a podcaster, and a professional voice actor. I started all of my businesses while WFH. Sure, I could have done all of those things as a work-from-the-office employee, but not as fast or as effectively. This is especially true when it comes to voiceover work, where auditions come at any time of day, and recording sessions must be done during normal business hours.

Who should be forced to go back to the office?

Some types of work are obviously most suited to be in-person, face-to-face. But what about those who have successfully been WFH during the pandemic with no adverse affects to the quality of their work or productivity with their teams?

It depends on whether your leadership values presenteeism over productivity.

First, the phrase “return to work” should be called “return to office” instead — we’ve been working hard at home too. Sometimes harder than when we were at the office (for those who don’t have strong boundaries around their work schedules and personal lives).

Purchased from Styled Stock Society

The Great Resignation is still in full effect. A recent Inc. article says that over 50% of employees plan to look for new jobs unless leaders do specific things regarding recognizing workers’ contributions, diversity and inclusion, and work/life balance. (The findings in that article came from a Workhuman research report).

I’ve been looking at some other recent reports too. Findings from the HubSpot 2022 Hybrid Work Report released in January reveal several interesting findings in its concise 8 pages.

They surveyed 4K workers in 8 markets, and reviewed comments from 6K hybrid-only employees. 39% wanted to stay at home, 18% want to go back, and the rest, 43% wanted to keep their hybrid arrangement. The most interesting stat I found: 36% said they’d rather go to the dentist very month than commute to work every day!

WFH is not necessarily isolating

Many of the HubSpot respondents felt disconnected or isolated from their teams by working from home, and this seems like a no-brainer. However, I wonder what about whether these folks’ personality types are introvert, extrovert or ambivert?

I’m an ambivert, and while I get a lot of energy from others, I cherish my alone time too. While I’ve dealt with chronic loneliness, I rarely feel lonely WFH. I prefer to be alone (not being alone means you’re lonely—it doesn’t). To be most productive, I need to work in a calm, quiet environment to concentrate.

Regardless of your preference to work with people around or not, you may not feel isolated just because you WFH alone.

Productivity and boundaries

Purchased from Styled Stock Society

Speaking of productivity, 70% of respondents said that too many calls and meetings are disruptive to their concentration and 58% feel at least half the meetings they joined could have been emails instead. 68% prefer only email or video calls for workplace communication.

I would think that Slack would be included in that preference. Like many tech companies, I work in a place where Slack is used heavily throughout the company instead of email. There are several of my co-workers have explicitly stated that they check their email very infrequently, and prefer Slack.

50% of respondents said their productivity is the same when WFH as when they weren’t, while 39% say their productivity has improved. But 58% still struggle with boundaries between work and personal life. It’s so easy to work longer hours in your house, with so many conveniences right there.

So again I ask: what’s more important at work: being physically present, or being productive?

“Anonymous” surveys, transparency
and implementation

31% of respondents (especially Black workers) said that opportunities to provide feedback anonymously would help them feel more supported and included at work. This is not a surprise to me! I just wonder how many of those surveys are counted. Are surveys sent to employees only, or contractors and freelancers too?

Where I currently work, I’m a contractor and not eligible for these kinds of surveys, even though I’m doing the same work as employees in my department. To give you an idea, there are about 20 writers in my department, but only 5 are employees. The rest of us are contractors. Therefore, 5 people are speaking for all of us, and we as contractors don’t have a voice, even though many of us stick around for more than 6 months, which is long enough to comment on what the work environment is like, whether we’re WFH or not. Contractors do all the same work that the employees do, dealing with most of the same problems, if not more.

People need to be able to provide feedback anonymously so they can feel free to express their true feelings without consequences or retaliation. I talked to a family member about this, and she said that some leaders have access to filter the results by cost center or department. (This makes the results seem less anonymous, especially if you have a department like the one I described with only 5 employees.) When you consider the limited space for answers, and no nuances from voice and tone, the answers can be taken the wrong way and easily misinterpreted. Sentiments like hers make her feel like it’s easier to not be 100% transparent, because otherwise, there could be repercussions.

Another important thing to consider is, who is implementing the feedback? When a company gets free-form responses where workers are expressing their pain points, what does the leadership do about it? How do you know if they plan to address the issues?

Valuing freedom and autonomy over money

Purchased from Styled Stock Society

Everyone needs money to live, and it’s certainly a big factor when deciding where, and for whom to work. But it’s not everything.

If your workers and business are thriving while WFH, let them continue to do so if they want. If they want to maintain a hybrid schedule where they work for only part of their week in the office, let them do so.

Bottom line? We all have choices. Let people do their best work in the best way for them.

My Trip to Havana, Cuba (Part 3 of 3)

by Ree Travel

Parts 1 & 2

Monday (Day 4) – Miramar

Santeria

The day before, someone in our group noticed that many Cubans were walking around wearing all-white clothing, and asked Monica about it. She said people who were preparing to enter the Afro-Cuban religion of Santeria had rules to follow, including all-white dress for a year, among other things. Our first stop on Monday was to learn about Santeria. We saw shrines and artwork in this area. (I didn’t film or photograph the interior.)

Santa Cana Studio

Next, we drove through Miramar for a bonus stop not on the itinerary: a visit to an independent artist’s studio, called the Santa Cana, near Playa del Este (Eastern beach of Havana). The artist’s name is Beatriz Sala, and she converted this house into a studio! It was breathtaking and meticulously designed.

Sala’s Santa Cana Studio in Miramar, Havana
Sala’s business card

She was preparing to exhibit at shows in Tennessee, Key West, and one other U.S. location that I can’t recall. I can’t imagine what she has to go through for Customs when traveling outside of Cuba with her art, but she’s a pro!

Fusterlandia

The artwork of Jose Fuster moves throughout his neighborhood! We walked around freely for about 30 minutes or so, capturing what we could of his unique handiwork here, and within the neighborhood.

My La Habera querida shirt from Muraleando
(I left the sticker on purpose)

Lunch at Vistamar

For lunch, we went to Vistamar along the waterfront. We were served with generous portions of croquettes and other appetizers, a butternut squash soup or Seviches (I had the former). For my our meal, we chose pollo, cerdo or pes (fish).

We all sat at this table with this view
The cerdo dish had 3 large chunks of pork & I could barely finish it

Angeles del Futuro

Our heartstrings didn’t stand a chance at the Cuban acrobatics school Angeles del Futuro, run by a man named Keeko. We watched 10 children aged 10 to 13 do amazing feats. These kids come from not-the-best of family environments and circumstances, so their performances were extra inspiring. Keeko said they practice for 3 hours a day after school.

(By the way, children in Cuba attend school for free throughout the island, including college, which they call University. Each afternoon, students get a break for 90 minutes at lunchtime to go home, and then they return for 2 more hours of school each afternoon.)

My sister and I shot video of each entire performance. Here are two from my sister on Instagram:

Most of their parents were in the audience and weren’t scared of their children getting harmed since they have been practicing for a long time, but we (on the tour) were in awe and somewhat nervous.

COVID Test & Status on the Island

There was conflicting information about the status of COVID in Cuba. Before we arrived, the STEP website said there was a travel advisory of Level 4 – Do Not Travel. (Almost every country had this status, actually.) However, Monica told us that the island is 90% vaccinated. Everywhere we went, we saw people with masks, even outside in the humid heat. The only time Cubans weren’t wearing masks is when they were talking to someone or eating, and even then, you could see their masks pulled down under their chins temporarily.

The trip paperwork explained that we had to have a PCR COVID tests 3 days before arrival and departure. But in actuality, we each went to the casa dining room, took a rapid antigen test, and tested negative.

I have 5 names, but the official mistakenly missed one of them when she filled out the certificate. To avoid any hassles, I asked her to correct it. She had to call someone to get permission, since each certificate has to be accounted for, and she had already listed a particular code number for me. But after a few minutes, she got the okay and filled out a new certificate. I’m called an “eagle-eye” for a reason!

Classic Car “Musical Chairs”

After a break, we all came back together for a final farewell dinner (except for the two who rode with us from the airport on day 1, because they go to bed early).

Me and my sister, about to take off from the casa!

We rode in style, all taking turns riding in 4 different classic taxis as we stopped in different points, including a parking lot near government buildings, and John Lennon park.

Final Farewell Dinner

Outside the restaurant

Our classic car ride ended in front of the restaurant for our fine farewell. Dinner started with a round of mojitos and a lesson about making cigars.

This woman is a professional cigar maker

Next, we ordered our food, and the owner, a very tall Olympian, greeted us with a little speech (he spoke English very well).

This art is just past the bar & our table was right behind it
orange soda
papaya juice
pork wrapped in chicken
This art is on the wall to the right of the entrance

A group called La Tradicional provided entertainment with their music and dancing. I was summoned to dance once again, wearing flip flops! But I got hearty applause.

We rounded out the night with a lesson about how to smoke your cigar with various flavorings such as dark chocolate, and a friendly debate about which rum was better: Havana Club or the rum from Santiago, Cuba. (Most Cubans that were present liked both, but voted for the latter.)

This cigar got home safely & I gifted it

We got back around 11 pm, and I remembered to finally get a picture with Monica. She went above and beyond for each of our needs, and I can’t express enough how wonderful and knowledgeable she is!

Me & Monica at the end of the night –
best tour guide ever!

Tuesday (Day 5) – Departure

Prices were listed in pesos but they accepted Euros

This was our travel day back home. Friendly Planet booked the flights for me and my sister to leave at 5:45 pm, so we had most of the day to ourselves. I was able to say goodbye to some of the others from our group as they left. My sister wanted to rest, so besides lunch, we stayed in our room until the taxi came to pick us up.

We planned to have one last meal at Michifu, but they were closed. I noticed a sign directly across the street for a cafeteria. The man who greeted us knew English, so I was able to order our food without any miscommunication. I got the arroz amarillo (yellow rice), perritos (hot dogs) and ensalada (salad).

Airport and Customs

I won’t go into a lot of detail about the happenings at the airport, but a few things of note:

  • At Customs, neither of the people guarding the entryway knew English. Only when I showed my Cuban VISA did they allow us through.
  • We had TSA Precheck and Global Entry, but neither was recognized there, so we had to remove our shoes, etc. after passing Customs.
  • We were grateful to get 30 minutes of free, reliable WiFi after being without it all week. All I had to do was enter my passport number on the main airport website. We couldn’t wait to get to our next airport (in Miami) so we could use our phones’ data normally.
  • We were at the airport 3 hours early, and there were only 2 places that were open near our gate to get food. My sister wanted a drink, so I waited in a very long line (where the card machine wasn’t working properly) to get her a small box of juice.
  • The toilets throughout the airport had no seats. If you can’t squat, you’re falling in.

Takeaways/Summary

My trip to Havana was life-changing. It really made me reflect on the things we’re taught about other cultures, as well as my American privilege.

This 5-day trip is the shortest trip that Friendly Planet offers at the time of this writing, and that includes a weekend, which is why my sister could come. Friendly Planet’s guides and itinerary were excellent, and they were flexible since a few of their former partners were still not open due to the pandemic. Monica was flexible based on what was open and available, and consistently gave us different things to see throughout the city, whether it was on the itinerary or not.

Culture:

  • The spirit of the Cuban people is alive and well. Despite their continued oppression and struggle to survive, they are lovely and hospitable people.
  • They teach English in school (which is completely free, including college, aka “University”), but they love when you indulge them and speak Spanish, enjoy their food, cigars and rum, and dance with them (which I did for 3 of the 4 days we traveled as a group).

History:

  • Cuban people love and revere Fidel Castro to this day, which is the opposite of what I learned growing up.
  • The history books in Cuba, just like in the U.S., don’t accurately teach the truth about the slavery of Africans how it really went down, including Christopher Columbus “discovering” Cuba and America.

Technology:

WiFi access is limited in Cuba. Their technology is behind, and most compatible with phones using 3G or lower. I’m so glad that I brought two phones because I ran out of space and couldn’t back up to my cloud with no internet.

Would I Return to Cuba?

YES! I am interested in visiting Santiago when foreigners are allowed to travel there (and not because their rum is better, lol). Currently, only the Havana airport is open for foreigners. Although, I could come back to Cuba and take a bus there—we saw some!

And I will most definitely travel with Friendly Planet again. I have many places I want to visit worldwide, but I think my next Latin trip will be somewhere in South America.

Vlog

Don’t miss the vlog with all the video I captured during this trip.

Vlog Timestamps (the bolded items apply to this blog for Days 4 and 5):

  • Walking tour 00:41
  • Farmer’s market 1:31
  • Parade 2:13
  • Salsa dancing lesson 3:36
  • Ernest Hemingway monument 4:39
  • Muraleando and rumba dancing 5:25
  • La Hotel Nacionale 13:52
  • Taxi ride 14:50
  • Fusterlandia 17:57
  • Angeles del Futuro 18:39
  • Classic car rides 52:39
  • Farewell and salsa dancing 1:00:39

My Trip to Havana, Cuba (Part 2 of 3)

by Ree Travel

(Part 1 in case you missed it.)

Saturday (Day 2) – Old Havana

I woke up feeling refreshed, and had breakfast at the casa.

Farmer’s Market and Walking Tour

This was the hottest, sunniest day of the trip, easily in the 90’s with high humidity.

We started by visiting a Farmer’s Market.

From there, we took our bus to an area that had hotels and what I’d call something like a strip mall, and walked around. (Some of the crosswalks had this funny animation of a man dancing when it was okay to cross the street.)

We also came across a mini-parade during our walk. Check the link at the bottom of this post to watch.

Sights from our Saturday tour

Clandestina

Our last stop before lunch was to Clandestina, Cuba’s first online clothing retailer and renowned design shop. The co-owner said they wanted to design and provide fashionable clothing for young people in Cuba. They paved the way for making modern Cuban fashion available online.

Lunch

I didn’t catch the name of the place where we had lunch. Many of the buildings in Centro Habana, particularly the businesses, didn’t have the name of the establishment on the outside. I had a cafe latte, and a cerdo sandwich with yuca.

History of Cuba in Photographs

Next, we visited the Raul Corrales Galeria in Habana Vieja. Raul was a famed photographer who captured several iconic pictures of Fidel Castro, Eduardo Che Guevara and others.

Raul’s granddaughter is the owner. She showed us around the gallery and told us the stories behind those iconic photos for about an hour.

La Casa de Son

Salsa lessons! This was the highlight of my day, even though we were really hot and tired when we arrived. I love to dance, take dance classes and teach a group exercise class, so I knew this would be fun anyway.

Even though I’m a good dancer, I never dance with a partner, so even I had something to learn here. And it was so much fun! We learned basic steps, and then danced casino style.

Donde Lis

The itinerary didn’t include visit to the Donde Lis restaurant, but Monica likes to over-deliver, and wanted to treat us all to a drink.

We got there around 4 pm, and it was only then that Monica told us it was her birthday. (Yes, she was treating us on her birthday!) Most of the group enjoyed another mojito, Cuba Libre (rum and Coke) or cerveza (beer), but my sister and I opted for helados (milkshakes). They were amazing!

My sister and I decided to go back to Michifu again for dinner with the ladies that we first joined from the airport. I had a lobster dish this time, not knowing that I would learn how to make it the very next day.

Sunday (Day 3) – Cojimar and Lawton

Cojimar

Cojimar is a local fishing village that was the setting for Ernest Hemingway’s book, The Old Man and the Sea. We visited his monument as Monica told us about his life and affinity for Cuba. We did not go to his house, but a couple of people on the tour had done so before and shared pictures.

Neighborhood Garden

The bus took us to a restaurant called Cafe Ajiaco for a cooking class. Another tour group arrived at the same time, and we all walked a few blocks away, with our guide Roy, one of the chefs. We met two neighbors named Julio and Jesus. They tend a large garden at Julio’s house, and they freely share the food and herbs they grow with the community, including this restaurant.

Cuban Cooking Class

Once we returned to Cafe Ajiaco, Roy introduced all of the staff, and the head chef did a demonstration for us. He showed us how he prepares ingredients for a particular kind of soup, which they later served to us.

Soup

Next, we went into the kitchen as a group to make our lunch, while the other tour group stayed in the main area of the restaurant to learn how to make mojitos. Half of the group were at a stove to cook a lobster dish—the same one I had a Michifu the night before. The other half of our group made a lamb dish.

When we finished cooking, we went to the bar so Roy (pictured center) could teach us to make mojitos. They gave us the muddlers as souvenirs.

Once we finished, it was time to eat. (I didn’t repost the lobster dish, but it’s similar to the one I posted from the previous day.) Buen provecho!

Muraleando

Our next stop was at Muraleando, a very interesting and inspiring community project. The folks here took a gigantic mountain of trash in the neighborhood and completely transformed it into the beautiful art museum that it is today. They have free enrichment classes for the kids in the community.

When we got upstairs, we were greeted with a bartender and a band! They offered us stiff drinks and played a few songs. Since the secret was out about my dance skills (from La Casa de Son the day before), I was quickly summoned to dance with the owners at the front of the stage. We did salsa and rumba.

We had a few minutes at the end of our visit to buy things from them, so my sister and I got t-shirts and artwork. (You can see one of the t-shirts within my pictures from Fusterlandia in Part 3 of this recap.)

Artisans Market

Our next stop was a 45-minute shopping spree at a flea market in either Old Havana or Lawton. My sister and I weren’t ready to leave during that time period, because we wanted to buy everything. It was so cheap! But we pulled it together. She converted our money to Euros for the trip, so she handled all the payments. I think she slipped each vendor something a little extra.

La Hotel Nacional

Our last stop of the day was at this infamous hotel that I somehow had never heard of. We walked around for while and had mojitos, and took a group photo.

We also observed the super high waves crashing over the sea wall of Malecom.

Taxi Back to Donde Lis

At our request, Monica a made reservation for me and my sister to go back to Donde Lis. Our pink taxi arrived with driver Eduardo, who was probably in his early 30s (most of the drivers we saw were much older, but it was a car he had in the family).

Although Monica gave him the street address, it was a little off, but he figured out how to get there. Then he went to a couple that was in our group who had a later reservation, parked and waited while we dined.

I ordered the Ropa Vieja Habanero (beef) and my sister ordered a chicken cordon bleu dish. We both drank glasses of malta with sweetened condensed milk. Yum!

One of the things we learned on this day was that toilets aren’t strong in Cuba. We were instructed to place all toilet paper in the trash and not flush it, even though it was barely 1-ply. However, there was no sign in our private bathrooms at the casa stating this, so I had been (successfully) flushing toilet paper normally. (The bathroom at Donde Lis had a clear sign explaining this, and when we went the bathroom in the airport on our way home the next day, I took a picture that further illustrates this point.)

On the way back to the casa, it started raining, so I helped Eduardo pull up soft top. Then we talked to Eduardo about how life in Havana has been during the pandemic, with no tourists and tight government restrictions on almost everything. We had a really nice and honest chat with him about all of that. If you’re ever in Havana and need a taxi, call Eduardo—he’s a reliable and friendly driver!

Unbeknownst to us, we were going to get a few more taxi rides the next day.

Part 3

Vlog

Don’t miss the vlog with all the video I captured during this trip.

Vlog Timestamps (the bolded items apply this the blog you just read for Days 2 and 3):

  • Walking tour 00:41
  • Farmer’s market 1:31
  • Parade 2:13
  • Salsa dancing lesson 3:36
  • Ernest Hemingway monument 4:39
  • Muraleando and rumba dancing 5:25
  • La Hotel Nacionale 13:52
  • Taxi ride 14:50
  • Fusterlandia 17:57
  • Angeles del Futuro 18:39
  • Classic car rides 52:39
  • Farewell and salsa dancing 1:00:39

My Trip to Havana, Cuba (Part 1 of 3)

by Ree Travel
Boarding pass with Cuba Ready stamp

My sister and I booked a group trip to Havana, Cuba in 2019. Due to the pandemic, we were unable to travel there until March 2022. And this was the first international trip we took since the pandemic started.

We spent 5 days in Havana from a Friday to a Tuesday. We were the first group to go to Cuba with this travel company, Friendly Planet, since the pandemic started.

This trip recap is based on my experience, thoughts and feelings as a somewhat privileged African American woman traveling to Cuba for the first time. All of the pictures and videos below were taken by me and my sister during our trip.

Friday (Day 1) – Arrival

Getting There

My sister and I left her house at 2 am for our first flight (to Miami). The airport’s website said that we should arrive there 3 hours early, and our flight was to leave at 5:12 am. However, the ticket counters don’t open until 3 am, so we still had to wait. We were asked for our passports, negative PCR COVID tests, and a form with a QR code specifically required to enter Cuba from one of their travel websites. The flight arrived in Cuba at about 7:45 am, and our flight to Cuba left at about 9:45 am, which was about an hour.

Customs is always fun, right? It seemed disorganized to me, and different from the customs process at the last island we visited in 2019 (Saint Maarten). First, we waited in line to show the form with the QR code to someone sitting at a desk with papers. Once she scanned and verified that code, I went to the next line. That person looked at my passport and took my picture (I had to take my glasses off for the picture). I showed her my Global Entry card (this is a step up from TSA Precheck), but she didn’t know what it was (therefore it wasn’t needed). (In Saint Maarten, we traveled as a group of 7 for a family vacation, and the customs officer took care of all of us as a group, but in Havana, the customs officer said that this “was personal” so she had to check travelers’ information individually.)

Next, I went to a line to place my items on a conveyor belt and walk through a metal detector. I waited for my sister to finish and come through her line, and then, within 20 yards of the exit door, two or three different people came to check our QR codes again (even though that was the first step of Customs).

Outside of Havana airport
Havana, Cuba airport

Just outside the door, I saw two women wearing bright gold shirts and holding signs for Friendly Planet. Their names are Mabel and Monica. Monica was our tour guide. They welcomed us and said that there were 2 more people from our group that they were expecting at any moment. They explained that even though the itinerary only mentioned a welcome dinner at a local paladar (restaurant), but Monica wanted to show us around Havana that day to “give us more,” and she was happy to do so. Funny enough, the 2 people we were waiting for arrived (a mother and adult daughter), and we found out that the mother lives in the same city we do!

We took our bags to the taxi bus (it was small like a taxi but shaped like a bus), and Mabel explained some things to us about what’s been going on in the island, for example:

  • They haven’t had many tourists (but would be happy to see Americans despite what we may have heard).
  • How to handle currency since Cuba accepts Cuban pesos, Euros and American dollars but in different scenarios. The paperwork we received from the travel agency said that American dollars were useless here. They advised us to bring cash in the form of Euros, and then have some of that exchanged for Cuban pesos. Also, Cuba does not use coins of any kind, only paper money.
  • How the government has so many sanctions and tightly controls supplies of everything from imports to food, rationing their food to people each day (people were in long lines each day to get food and medicine).
  • Why we were seeing so many people in crowds on the street trying to hitchhike and wave down drivers (all the city buses were overcrowded).

Lunch, WiFi Rules and Getting Settled

Hostel entrance

The drive from the airport to the casa (our hostel) took about half an hour. It was very hot and humid, about 80 degrees or so. After we checked in it was time for lunch, so the four of us (me, my sister, and the mother and daughter who rode the taxi bus with us) walked to lunch at a place a few blocks away called Michifu (pronounced as me-chee-foo). A portion of the restaurant had no roof, so to avoid the sun, we sat in a covered area near a large fan.

Entrance to the Michifu restarurant

When we paid the bill, it showed the amount in both Cuban pesos and in American dollars. We couldn’t believe how cheap it was: my sister and I each had an alcoholic beverage and a meal, and the total was less than $25! The server said they had to keep their prices low so that the locals could afford to come, and they could stay in business. My sister carried all our money, so she tipped them and others generously everywhere we went, as our privilege was so obvious, and the Cuban people were so gracious to us.

When we returned to the casa after our meal, there were 6 or 7 others standing in the lobby, which was the rest of our group. We introduced ourselves, and Monica made some announcements about what to expect for the rest of the day. The person running the casa said that we could only use the WiFi for 4 hours each day: from 8 to 10 am, and 7 to 9 pm. Unfortunately, we found that this was inconvenient because 1) we weren’t usually in the casa during those hours, and 2) when we were, the WiFi was very slow. (The international plans from both my carrier and my sister’s had outrageous prices to use data, calls and texts, so we didn’t bother.) I brought two phones (one of which doesn’t have data/service) just to make sure I didn’t mistakenly use data.

We had about 2 hours before we were going to leave for a walking tour and dinner together, so my sister and I went back to our room and took a much-needed nap (we left at 2 am, remember?). We were exhausted, but grateful that there was no time zone change in Cuba from where we live.

Because Cuba is an island, and the government owns all of the real estate, homes and buildings are built high, not wide. Our room had a double bed as soon as you walked in, and then to the right was a long flight of stairs to a twin bed. The stairs are only half the size of your feet, so I had to use extreme care every time I used them, including when I had to use the bathroom in the middle of the night. Thankfully I never fell, and I didn’t stop up the toilet. (For awhile, we weren’t told about the toilets here, which can’t handle toilet paper.) The fan in the main area and a small fan near the twin bed helped us sleep comfortably. We also received new water bottles every day, as Monica told us not to drink the tap water.

Walking Tour and Dinner

Statute of Jesus Christ

Monica is a native of Cuba, and a wealth of information. We left the casa around 4 pm and started at the Christ statue, and went from there. (For the rest of this article, I won’t try to explain or recap every detail, but included pictures and videos to give you some idea of what we saw and heard.) At the end of the walk, we were joined by a member of Friendly Planet’s team, Alison. She had lived in Cuba for 5 years, spoke Spanish fluently, and made many connections. Along with Monica, she participated as part of the group, but added lots of insight and storytelling about the Cuban culture throughout our trip.

We had dinner at El Carbon, and they served very generous portions of appetizers like croquetas (croquettes), yuca, cerdo (pork) and pollo (chicken). They gave us so much food, we insisted Monica take it home. She shared it with her kids and neighbors, who were very grateful.

El Carbon restaurant entrance

My sister and I had some mixed feelings about turning food away, because Monica explained how hard it was to get it in Cuba. We certainly didn’t want to offend anyone, especially when they were so sweet and hospitable to us.

Ready for Part 2?

Vlog

Don’t miss the vlog with all the video I captured during this trip.

Vlog Timestamps (the bolded items apply this the blog you just read for Day 1):

  • Walking tour 00:41
  • Farmer’s market 1:31
  • Parade 2:13
  • Salsa dancing lesson 3:36
  • Ernest Hemingway monument 4:39
  • Muraleando and rumba dancing 5:25
  • La Hotel Nacionale 13:52
  • Taxi ride 14:50
  • Fusterlandia 17:57
  • Angeles del Futuro 18:39
  • Classic car rides 52:39
  • Farewell and salsa dancing 1:00:39

Copywriting Tip for VO Scripts

by Ree Copywriting
woman's hand turning the page of a script

As someone who does both copywriting and voiceover professionally, I wanted to share a crossover tip for those of you who write copy for commercials or e-learning scripts.

I recently decided to flip the script, and start writing video scripts. I usually read them as voiceovers, and I have some opinions about how to “ree-mix” them.

Too Many Words in Sentences

A huge problem I encounter with scripts in a voiceover recording session, is that there are too many words crammed into a sentence, and usually without the correct punctuation. This often happens when there is a tight time deadline and all the words need to be read in a specific amount of time, and still sound natural. Conversational. Like you’re talking to a friend.

Often, the scripts are revised until the last minute so it doesn’t make a lot of sense to rehearse beforehand.

These copywriters are writing scripts for the eye instead of writing for the ear.

Please copywriters, educate your clients: Let them know that they may not be able to get all of their talking points in a 15- or 30-second commercial without cutting some words.

Simple Sentences Make a Big Impact

How do you know a sentence needs revising? Simply read your copy out loud and time it. If you’re getting hung up on words, have to stop and keep taking breaths, and or have to read fast to get it all in your designated time limit, then guess what? You need to rewrite your copy. Use a tool like Hemingway to check the complexity of your sentences, grammar, and grade level of your writing.

We will all save so much time in recording sessions. Your voice talent will really appreciate you and be more efficient, giving you the best performance and a shorter time.