Tech’s Revival of Tradition: A Paradigm Shift in Pharma
Machine Learning’s role in advancing sustainable drug discovery.
The essence of humanity intertwines with innovation — a continual process of generating ideas that redefine our lives. More crucial than any single innovation is the framework that nurtures them. I aim to initiate a wider conversation about innovation generation by examining the research and development landscape within the Pharmaceutical Industry and its Western Development. My journey into this field commenced at a young age, assisting at my father’s pharmacy at just 10 years old. This early exposure fostered a deep curiosity about the origins of medication, leading me to pursue this passion academically and culminating in my MS in Pharmaceutical Science. I believe our current approach to drug discovery is still in its early stages and is poised for a technological revolution.
https://www.youtube.com/watch?v=-5KqYUqPmDY
The pursuit of new medication involves intricate stages — research, testing, and rigorous evaluation — culminating in its dispensation at pharmacies. Yet, delving into this expansive journey lacks allure and exceeds our present scope. Instead, I’m drawn to the genesis of discovery — the pivotal event igniting this sequence.
Some Definitions
- Bioprospecting is the exploration of natural sources for potential drugs, such as plants, animals, microorganisms, and marine organisms.
- Serendipity is the occurrence of unexpected and beneficial discoveries. Serendipity has played a significant role in drug discovery- the story of Penicilin is a great example.
- Ethnobotany is the study of how different cultures use plants for various purposes, such as food, medicine, clothing, and rituals.
- High-throughput screening (HTS) is a technique that allows the rapid testing of large numbers of chemical compounds for their biological activity against a target of interest. HTS typically utilizes automated robots, micro-plates, and specialized software.
- High-throughput virtual screening (HTVS) is a computational method that simulates the interaction of chemical compounds with a target of interest, using molecular modeling and docking algorithms. HTVS can complement HTS by reducing the number of compounds that need to be tested experimentally, as well as by expanding the chemical diversity and novelty of the compounds.
Methods in Drug Discovery
Pharma’s innovation pipeline has experienced significant evolution in the last century. Despite the existence of pharmaceutical innovations predating the Scientific Method, the value of such innovation-driven strategies is often overlooked. Before the Age of Enlightenment, pharmaceutical advancements relied on three primary methods: sourcing materials from nature (Bioprospecting), recognizing unexpected therapeutic events (Serendipity), and learning from the therapeutic relationship in ethnobotany.
Over time, new avenues for more efficient drug discovery emerged. The first major turning point was the inception of synthetic chemistry, liberating chemists from the limitations of existing molecules by enabling the transformation of known molecules into novel ones. Subsequently, significant advancements in robotics (HTS) exponentially increased screening capabilities and reduced dependency on human labor. Additionally, the evolution of computers allowed for the simulation of novel compounds and examination of their biochemical interactions with target receptors without the need for synthesis (HTVS).
Throughout these developments, the philosophy of drug discovery shifted from reliance on trial-and-error and serendipity to Rational Drug Design. This approach involves developing medications based on a comprehensive understanding of the structures and functions of target molecules, including receptors, transporters, and transcription factors.
Pharma is ‘High’ on Tech
As we scrutinize the evolution of innovation in the pharmaceutical industry, one fact stands out: the move to minimize reliance on nature. It’s fair to acknowledge the unpredictability and difficulty in controlling nature. Theoretically, reducing our dependence on nature for drug candidate sourcing and biological targets, as well as diminishing the labor-intensive testing processes, should make medicine more accessible and diverse in therapeutic options.
This argument holds weight, explaining the industry’s shift in this direction over the past few decades. The current landscape of pharmaceutical innovation follows a distinct path:
- Identifying a problem based on understanding the pathology and gauging the expected economic value of the solution.
- Developing or acquiring access to extensive chemical or virtual molecular libraries.
- Testing the library contents against biological targets.
- Identifying and singling out the most promising candidates.
- Creating and testing variations of the candidate to optimize desired interactions and minimize undesired ones.
- Advancing the finalized candidate into animal trials and eventually human trials.
Conquering the Darkness
While the outlined process has undoubtedly streamlined the identification and refinement of potential drug candidates, it’s crucial to acknowledge its constraints, particularly in terms of fostering true innovation.
One primary limitation lies in the potential for this approach to prioritize incremental improvements over groundbreaking innovation. Relying heavily on known chemical libraries and established biological targets might limit the exploration of entirely novel mechanisms or undiscovered pathways that could lead to transformative medications. This process tends to emphasize refining existing candidates rather than venturing into uncharted territories, potentially hindering the development of truly innovative treatments.
Moreover, the reliance on established models and known targets might lead to a lack of diversity in the types of drugs being developed. This approach could inadvertently overlook unconventional agents or unconventional targets that could hold immense therapeutic potential but haven’t been explored due to their unpredictability or unfamiliarity.
Despite the efficiency and optimization offered by these steps, there’s a looming risk of narrowing the scope of drug discovery to familiar paths, potentially stifling the emergence of groundbreaking innovations that could revolutionize medical treatment.
“We shall not cease from exploration, and the end of all our exploring will be to arrive where we started and know the place for the first time.” T.S. Eliot
Contrary to common belief, the vast majority of higher-level plant species — encompassing angiosperms, gymnosperms, and other categories like fungi, lichen, and algae, which are even more enigmatic — remain largely unsampled and unexplored. Although slightly dated, estimates from 2012 shed light on a startling fact: a mere 6% of these plant species have undergone screening for biological activity, and less than 15% have been assessed for phytochemical activity.
This glaring gap underscores the immense untapped potential harbored within these unexamined species. They represent a treasure trove holding promises of new medicines, sustainable resources, and invaluable ecological insights. The urgency lies in the need for further investigation and preservation of Earth’s diverse flora. Doing so isn’t just about advancing our scientific understanding but also safeguarding the well-being of our planet.
Within these uncharted territories of the plant kingdom and other lesser-known botanical realms, lies a potential reservoir of compounds that could revolutionize medicine. Nature has proven time and again that it holds the key to unique and potent bioactive compounds. Furthermore, tapping into these unexplored species not only opens avenues for innovative drugs but also offers sustainable solutions, potentially easing the strain on overexploited resources.
Urgent and concerted efforts are needed not only to explore and understand these uncharted territories but also to preserve them. This calls for a collaborative approach involving scientists, conservationists, policymakers, and local communities worldwide. By unlocking the secrets hidden within these largely unexplored species, we can not only enhance our scientific knowledge but also secure a sustainable and healthier future for our planet and its inhabitants.
Modern Tools, Ancient Strategies
Integrating modern tools like machine learning with traditional drug discovery methods, such as bioprospecting and ethnobotany, presents a compelling avenue to unveil innovative drug candiates that might have been overlooked within the confines of outlined steps.
Ethnobotany and bioprospecting, rooted in centuries-old practices, have historically tapped into nature’s vast reservoir of medicinal plants and natural compounds. However, the vastness of natural biodiversity poses challenges in efficiently exploring and identifying potential therapeutic agents. Here, machine learning algorithms can revolutionize the process by analyzing vast datasets of botanical information, indigenous knowledge, and biological activities of compounds, effectively accelerating the identification of promising molecules from natural sources. By discerning patterns and relationships within this wealth of data, machine learning models can highlight overlooked compounds with unique medicinal properties, guiding researchers towards novel drug candidates that might have eluded conventional screening methods.
Furthermore, machine learning augments these traditional methods by predicting and simulating the interactions between natural compounds and biological targets. Instead of solely relying on empirical testing, computational models powered by machine learning can forecast the potential efficacy and safety profiles of natural compounds. This predictive capability not only expedites the selection of candidates but also reduces the resources and time required for exhaustive laboratory experiments.
In essence, the fusion of machine learning with traditional drug discovery methods represents a promising paradigm shift, amplifying the efficiency and scope of exploration within natural sources while facilitating the identification of novel therapeutic agents. This collaborative approach holds the potential to unveil a treasure trove of innovative drug candidates, harnessing the strengths of both traditional wisdom and modern computational prowess to revolutionize the landscape of drug discovery.
Fit within the system
ML-inspired bioprospecting and ethnobotany can seamlessly integrate into the broader pharmaceutical drug development pipeline, complementing High-Throughput Screening (HTS) and High-Throughput Virtual Screening (HTVS) methodologies. These traditional and modern approaches can synergize to create a more comprehensive and efficient drug discovery process.
HTS and HTVS excel in rapidly screening vast chemical libraries and simulating molecular interactions, respectively, to identify potential drug candidates. Integrating ML-enhanced bioprospecting and ethnobotany into this pipeline enriches the initial pool of compounds by leveraging historical knowledge and natural sources. By using machine learning algorithms to analyze vast datasets of biological information, traditional remedies, and indigenous knowledge, researchers can pinpoint promising natural compounds with therapeutic potential. This augments the chemical libraries utilized in HTS, offering a more diverse and enriched starting point for screening.
Furthermore, the insights gleaned from ML-enhanced bioprospecting and ethnobotany can guide HTVS by providing a deeper understanding of the molecular interactions between natural compounds and biological targets. This integration not only expands the scope of candidate compounds but also enhances the precision and efficiency of identifying molecules with high binding affinity and desirable pharmacological properties. Ultimately, this collaboration between traditional methods and modern technology enhances the drug development pipeline, potentially uncovering novel and effective medications for various diseases.
Scale of Impact
Not only does this adoption shed light on forgotten corridors and expand the healing potential of medicine, but the integration of ML-enhanced bioprospecting (and to a lesser extent, ethnobotany) extends its transformative influence across diverse industries beyond pharmaceuticals. The modified application of this concept has the capacity to generate novel products in cosmetics, fragrances, agriculture, and even material engineering.
The integration of ML-enhanced bioprospecting transcends its immediate impact on medicine and extends its transformative potential across a spectrum of industries. By exploring forgotten corridors of traditional knowledge and merging it with machine learning, this approach unlocks innovative solutions in diverse sectors. In cosmetics, fragrances, agriculture, and material engineering, this adoption fosters the discovery of new raw materials, formulations, and techniques. It revolutionizes product development by incorporating natural elements and historical insights into the creation of cosmetics, fragrances, agricultural practices, and sustainable materials. This broader application not only diversifies industry offerings but also demonstrates the adaptability and wide-reaching implications of merging traditional wisdom with cutting-edge technology, shaping a landscape of innovation across multiple sectors.
ML-inspired bioprospecting is part of a larger industrial revolutution, its impact spanning public health advancements and potentially addressing health challenges while bolstering food security. Societally, this convergence preserves cultural heritage while advocating for sustainability, striking a harmonious balance between progress and tradition
This blog post serves as just the tip of the iceberg in our journey. The intersection of traditional wisdom with the power of machine learning promises a landscape of innovation across multiple industries, transcending the realms of medicine to influence cosmetics, fragrances, agriculture, material engineering, and beyond. Stay tuned for an exciting series of deep dives into this fascinating fusion of age-old wisdom and cutting-edge technology. In upcoming posts, we’ll delve deeper, uncovering more insights, case studies, and the far-reaching impact of merging traditional knowledge with the capabilities of modern data-driven methodologies.
