Dec, 19 2022

Transformation of Drug Discovery through Artificial Intelligence

Recently, the use of artificial intelligence (AI) is increasing at a fast pace. Almost in every field, the use of AI is increasing. With its adaptation, many things are becoming smoother. As the hype around AI has accelerated, big market players and merchants have been scrambling to promote how their products and services use AI. Artificial intelligence is the recreation of human intelligence processes by machines, mainly through computer systems. Usually, often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a combination of hardware and software for writing and training machine learning algorithms. A few programming languages similar to AI, such as Python, R and Java, are popular.  

Our DBMR team has investigated the machine learning operationalization software market and witnessed that North America dominates the machine learning operationalization software market during the forecast period of 2022-2029 and will continue to flourish its trend of dominance during the forecast period due to the presence of major key players and increase in the number of technical innovations in this region. The market is expected to exhibit a CAGR of 44.7% for the forecast period of 2022-2029.

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History of AI

Although AI has become more prevalent recently due to increased data volumes, advanced algorithms, and computing power and storage improvements, the term was introduced in 1956. At that point in time, they explored subjects such as problem-solving and symbolic methods. In the 1960s, the US Department of Defense took a genuine interest in this field and began training computers to mimic basic human reasoning. For instance, the Defense Advanced Research Projects Agency (DARPA) finished the street mapping projects in the 1970s. This early work built the path for the automation and formal reasoning visible in computers today, including decision support systems and smart search systems designed to complement and enhance human abilities.

How AI is Changing Our World

AI is blessing our lives with significant advantages such as online search recommendations, chatbots, voice-assistants, and much more. With each passing day, it is becoming an integral part of our lives. AI will have tremendous benefits in the future as it will lead to higher production rates and higher productivity in varied sectors. In the present time and also in the near future, automation powered by artificial intelligence is time-consuming. Hours of manual work can be automated. It is applicable everywhere. It can be used everywhere, predicting traffic or weather conditions. The use of automation in AI is one of the major boons amongst the others.

Advantages of Artificial Intelligence

Pharmaceutical Market of AI at a Glance

  • Reduction in Human Error

Artificial intelligence is beneficial in reducing the so-called "Human error." Humans are bound to make mistakes, but that is not the case with computer systems. Computers do not make these mistakes if they are correctly programmed. AI is performed beneficially by applying previously gathered information through a certain set of algorithms. Hence, errors are minimized in this regard, and the possibility of a higher degree of precision becomes higher.

  • Takes Risks Instead of Humans

One can overcome several risky restrictions of humans with the help of an AI robot which in turn can do the difficult things for us, and this is one of the most significant advantages of artificial intelligence.

For instance, if we go back and remember the Chernobyl nuclear power plant explosion in Ukraine, there were no AI-powered robots at that time that could help us to minimize the effect of radiation in that situation; AI robots could have been a savior to the massive crowd by minimizing the fire. AI robots can be used in such cases where intervention can be hazardous.

  • Full Availability

If we keep the breaks aside, an average human will work approximately 4–6 hours daily. Working all day long gets difficult and impossible for humans. Maintaining the work-life balance, handling personal responsibilities, and the tedious work pressure is hard. Sometimes some work is essential and needs to be finished in a particular timeline, but it is sometimes impossible. Using AI, we can make machines work 24x7 without any breaks, and they don't even get bored, unlike humans.

  • Assists Research

AI enables researchers to surpass the large volume of data from various sources. With real-time data, research can benefit from the wide body of information available, as long as it's easily translated. Medical research institutes such as the Childhood Cancer Data Lab are developing useful software for healthcare professionals to better steer wide data collections. AI has also been used widely to assess and detect symptoms to prevent the disease progression. Telehealth solutions are being executed to track patient progress, recover vital diagnosis data and aid in population information to shared networks.

  • Reduce Physician Stress

According to some latest research reports, more than half of primary physicians feel stressed from deadline pressures and other workplace factors. AI aids in streamlining procedures, automating functions, instantly sharing data and organizing operations, which generally helps physicians avoid juggling things. However, AI can assist with more time-intensive operations, explaining diagnoses, for instance, medical professionals may experience some stress alleviation."

  • Safer Surgeries

Surgeons get an increased level of skill to operate in small spaces that might otherwise require open surgery. AI is helping in this regard, finding its suitable place in healthcare robotics by contributing to its appropriate need in surgery. Robots can be more accurate around sensitive organs and tissues, reduce the risk of infection, post-surgery pain and reduce blood loss. Robotic surgery involves more advantages, such as less scarring and shorter recovery times due to smaller incisions required. For instance, the Maastricht University Medical Center in the Netherlands used an AI-assisted robot in 2017 to suture small blood vessels, some larger than .03 millimeters. The robot is handled and managed by a surgeon whose hand movements are converted into precise actions performed by robot hands.

Our DBMT team has investigated the gynecology robotic surgery market and witnessed North America dominating the gynecology robotic surgery market because of the increasing demand for minimally invasive surgery among the population within the region. Asia-Pacific is expected to witness significant growth during the forecast period due to the growing awareness about women's health and healthcare expenditure in the region. Some of the major players operating in the gynecology robotic surgery market are BOWA-electronic GmbH & Co. KG, Prima Medical, XCELLANCE Medical Technologies, ATMOS MedizinTechnik GmbH & Co. KG, Ethicon US, LLC., Johnson & Johnson Services, Inc., Parkell, Inc.

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  • Increased Prevention Care

AI and machine learning assist with infectious disease prevention and management. The outbreak intelligence platform, Blue Dot, helps to analyze airline ticketing and flight paths for the accurate prediction of the path of COVID-19 from Wuhan to Bangkok, Seoul, and Taipei. Likewise, AI-enabled systems can help physicians to detect the spread of disease when patients enter an emergency room with a rapid diagnosis to enable effective isolation and quarantine procedures.

  • Reduce Overall Costs

AI helps greatly reduce the time consumed in performing particular processes and the cost of the processes. For instance, AI can analyze millions of images for the detection of signs of the disease. It removes the costly manual work involved. Patients are treated quickly and more effectively, imposing several advantages such as decreasing admissions, waiting times, and the need for beds.

A recent study predicted substantial cost savings in multiple areas from AI automation which are:

  • Dosage error reduction – $16 billion
  • Robot-assisted surgery – $40 billion
  • Administrative workflow assistance – $18 billion
  • Virtual nursing assistants – $20 billion
  • Fraud detection – $17 billion

Our DBMR team has investigated the minimally invasive medical robotics, imaging and visualization systems and surgical instruments market and witnessed that the market to account to USD 91.22 billion by 2028 and will grow at a CAGR of 8.6% in the above-mentioned forecast period. North America region leads the minimally invasive medical robotics, imaging and visualization systems and surgical instruments market owing to the region's high rate of accidental injuries and large geriatric population. Asia-Pacific is expected to expand at a significant growth rate over the forecast period of 2021 to 2028 owing to the road crashes, increasing geriatric population in Japan and China, and emerging economy are expected to promote the emergence of MIS procedures in this particular region.

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AI into the Field of Healthcare

AI's huge involvement in developing a pharmaceutical product gives it a rational drug design; help in decision making; understands the right therapy for a patient, including personalized medicines; and manages the clinical data generated and used for future drug development. For instance, E-VAI is an analytical and decision-making AI platform that is developed by Eularis, which uses machine learning algorithms to create analytical roadmaps based on competitors, key stakeholders, and currently that held market share to predict major drivers in sales of pharmaceuticals, which helps the marketing executives to assign resources for utmost gain of the market share and also enabled them to expect where to make investments.

AI is playing a vital role in drug discovery. AI can recognize hit and lead compounds, provide a faster validation of the drug target within a short time, and optimize the drug structure design. It has wide applications in varied aspects of drug discovery. It is explained below:

Pharmaceutical Market of AI at a Glance

Despite the advantages faced by AI, it has some significant data challenges, such as the data's scale, growth, diversity, and uncertainty. The data sets available for drug development in various pharmaceutical companies can involve millions of compounds and conventional ML tools that can't deal with such problems.

For instance, a quantitative structure-activity relationship (QSAR)-based computational model can predict large numbers of compounds or simple physicochemical parameters, such as log P or log D, in a short time. In addition, QSAR-based models also face serious issues such as experimental data error in training sets, small training sets, and lack of experimental validations.

There has been an introduction of numerous in silico methods and virtual screen compounds from virtual chemical spaces, which, combined with the structure and ligand-based approaches, gives a better profile analysis, faster elimination of non-lead compounds and selection of drug molecules with reduced expenditure. Drug design algorithms, such as coulomb matrices and molecular fingerprint recognition, consider the physical, chemical, and toxicological profiles to helps in selecting a lead compound.

Our DBMR team has investigated the in-silico drug discovery market and witnessed that north america region leads the in-silico drug discovery market owing to the rapid technological advancements, strong presence of strong vendors and the presence of large patient population suffering from various chronic and infectious diseases. Asia-Pacific is expected to expand at a significant growth rate because of the rise in the number of academics and extensive research in cancer and diabetes. Also, the rise in the high growth in the biomarker identification area and the focus on reduction in readmission rates and medical errors are also expected to contribute to the growth in the global market.

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List of AI Tools Used in Drug Discovery

Various AI tools are widely used in drug discovery. Several web-based tools, such as LimTox, admetSAR,Toxtree and pkCSM are available to help reduce the cost of many different assays. Advanced AI-based approaches mostly look for compounds' similarities or predict the compound's toxicity based on input features. Another such instance of a tool is eToxPred, which helps to estimate the toxicity of the compounds and synthesis feasibility of many small organic molecules and accuracy as high as 72%. Many other tools are also present that helps in predicting the toxicity of the compound. Many times, some of the FDA approval drugs have serious adverse events that need to be predicted as early as possible; these AI tools are used in this regard. AI tools are a wide range of sets, but herewith we mention some of the tools:

Pharmaceutical Market of AI at a Glance

Pharmaceutical Market of AI at a Glance

Many pharmaceutical companies are shifting towards AI to reduce the financial cost and chances of failures associated with the experiments. There was an increase in the AI market from US$200 million in 2015 to US$700 million in 2018, and it is predicted to reach upto $5 billion by 2024. AI is expected to revolutionize the pharmaceutical and medical sectors and is projected for a 40% growth from 2017 to 2024. Many pharmaceutical companies have made big-time investments and are continuing to invest in artificial intelligence and have teamed up with multiple AI companies to develop essential healthcare tools. For instance, there has been a collaboration of DeepMind Technologies, a subsidiary of Google, with the Royal Free London NHS Foundation Trust, which has been used to assist with acute kidney injury. Another example is Boehringer Ingelheim and HealX, which collaborated to find therapies for rare neurological diseases. Eli Lilly and Company and Atomwise have collaborated to develop drugs on novel protein targets. Another one on the list is the collaboration of Mateon Therapeutics and PointR Data, which helped treat late-stage melanoma, pancreatic cancer, and glioma. F. Hoffmann-La Roche and Owkin have conducted many clinical trials based on machine learning algorithms.

AI-Based Advanced Applications

  • AI-based Nanorobots for Drug Delivery

Nanorobots are designed mainly comprised of integrated circuits, sensors, power supply, and secure data backup, which are maintained via computational technologies, such as AI. They are programmed to avoid collision, target identification, detect and attach, and finally excretion from the body. The latest advancement in nano/microrobots allows them to navigate to the targeted site based on physiological conditions, such as pH, improving efficacy and reducing systemic adverse effects.

Many parameters need to be considered, such as dose adjustment, sustained release, control release, and the release of the drugs that need to be controlled for the appropriate delivery of drugs. Microchip implants are used for the programmed release of the implant as well as to detect the appropriate location of the implant in the body

Our DBMR team has investigated the nanorobots market and witnessed that North America dominates the nanorobots market due to the rise in the adoption of nano-robotics technology. Furthermore, the presence of sophisticated healthcare infrastructure will further boost the growth of the nanorobots market in the region during the forecast period. The growing application areas of microscopes and incorporation of microscopy with spectroscopy are further estimated to provide potential opportunities for the growth of the nanorobots market in the coming years.

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  • AI Emergence in Nanomedicine

The use of nano-technology is definitely on the rise. Scientists are relying on and involving more and more of this methodology in the field of medicine. Nanomedicines are used to diagnose and treat many complex diseases, namely HIV, cancer, malaria, asthma, and various inflammatory diseases. In recent years, nanoparticle-modified drug delivery has become necessary in the field of therapeutics and diagnostics due to its enhanced efficacy and treatment. If nano-technology is mixed with AI, it can solve many issues in formulation development. For instance, AI-assisted the preparation of silicasomes. Silicasomes are a combination of iRGD, a tumor-penetrating peptide, and irinotecan-loaded multifunctional mesoporous silica nanoparticles. Nanomedicines have increased the uptake of silicasomes three to four times as iRGD helps in the improvement of transcytosis of silicasomes.

  • AI in Combination Drug Delivery and Synergism/Antagonism Prediction

Several new combinations of drugs have been approved and marketed to treat complex diseases, such as tuberculosis and cancer, as they can provide a synergistic effect for quick recovery of the patients. The potential drugs chosen for this combination require high-throughput screening of a considerable number of drugs, thus making the process tedious. For instance, cancer therapy involves a combination of six or seven drugs. Rashid et al. developed a model of quadratic phenotype optimization platform, which is used to detect optimal combination therapy for treating bortezomib-resistant multiple myeloma through a collection of 114 FDA-approved drugs. The best two drugs involved in this model are decitabine (Dec) and mitomycin C (MitoC).

Besides the advanced applications of AI, it also has importance in market positioning. With the ease of technology and e-commerce, it has become easier for all companies to publicize their brand on the public platform. One of the most used tools is SEO, which most companies use to occupy a fixed position in online marketing and help position the product in the market. Companies constantly try to manage their position at a higher position in the game, giving recognition to their brand in a short time.

Our DBMR team has investigated the e-commerce packaging market and witnessed that asia-pacific dominates the e-commerce packaging market in terms of market share and revenue and will continue to flourish its dominance during the forecast period. This is due to the rising consumer preference towards corrugated boxes in growing countries such as India, China, and Japan. , China is leading the Asia-Pacific market. Covid-19 boomed the market growth. Covid-19 restricted the movement of man and materials. E-commerce played an important role in the pandemic because the demand for essential goods such as groceries, medicine, vegetables, and other product increased.

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With the advancement of artificial intelligence, and its remarkable tools, pharmaceutical companies are are getting advantageous in many aspects. It impacts the drug development process along with the product's overall lifecycle, which in return easily explains the rise in the number of start-ups. The healthcare sector faces many challenges, such as the increased cost of drugs and therapies. Society needs significant changes in this area, which must be given importance. As the era of digital health is increasing, and the prevalence of AI is increasing, personalized medications are also coming into light with the desired dose, release parameters and other required aspects that can be manufactured according to individual patient needs. AI-based technologies will not only help in speeding up the time needed for the products to become live in the market, but in addition to this, they will also help in the betterment of the products and the overall safety of the production process.

Furthermore, it will also provide better utilization of cost-effective and available resources, thereby increasing the importance of automation. Besides this aspect, the most significant worry associated with implementing these technologies is the job losses that would follow and the strict regulations required for the operation of AI. However, these systems aid in encouraging simplicity in humans and do not completely replace them. Many merchants include AI components in their standard offerings or provide access to AI-as-a-service (AIaaS) platforms. Their hardware, software, and staffing costs for AI can get expensive. The significance of AIaaS is that it allows individuals and companies to experiment with AI for several business purposes. The various sub-fields of AI, namely machine learning, neural networks, and deep learning are also equally helpful in drug discovery. Besides these, several other technologies support and enable AI, namely computer vision, the internet of things, advanced algorithms, and graphical processing units.

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