APR March 2024 - 32

» FORMULATION AND DEVELOPMENT
»
AI and Drug DiscoveryTools
and Techniques
AI involves several methods, such as reasoning, knowledge
representation, solution search, and a fundamental paradigm of ML
that comprises algorithms to recognize patterns within a set of data
that has been further classified. A subfield of ML is deep learning (DL),
which engages gradient descent in architecturally different models
such as artificial neural networks (ANNs) and Transformers using
supervised or unsupervised training procedures.
There are different types of ANNs used in deep learning, such as
multilayer perceptron (MLP) networks, recurrent neural networks
(RNNs), and convolutional neural networks (CNNs). At the same time,
popular Transformer architectures involve elements such as multihead
self-attention (MHSA), layer normalization (LN), and positional
embeddings (PE).
Several tools have also been developed based on the networks that
form the core architecture of AI systems. One such tool developed
using AI technology is the IBM Watson platform in the US. It was
designed to assist in the analysis of a patient's medical information
and its correlation with a vast database, resulting in suggestions for
treatment strategies for cancer.
One key application of AI is the prediction of the efficacy and toxicity
of potential drug compounds. Based on the analysis of a large
amount of experimental data, AI algorithms can identify relevant
and predictive molecular features that may not be apparent to
human researchers. This advanced analytical capability facilitates the
discovery of novel bioactive compounds, promising minimized clinical
side effects. Moreover, AI-driven methods significantly accelerate the
drug discovery process, outpacing traditional approaches with their
efficiency and precision.
Another important application of AI in drug discovery is the
identification of drug-drug interactions that take place when several
drugs are combined for the same or different diseases in the same
patient, resulting in altered effects or adverse reactions. This issue can
be identified by AI-based approaches by analyzing large datasets of
known drug interactions and recognising the patterns and trends.
Added to the key applications of AI in drug discovery is the design of
novel compounds with specific properties and activities. For example,
a deep learning (DL) algorithm has recently been trained on a dataset
of known drug compounds and their corresponding properties to
propose new therapeutic molecules with desirable characteristics
such as solubility and activity, demonstrating the potential of these
methods for the rapid and efficient design of new drug candidates.
Key Advancements by Frontrunners
The CPHI Report's AI findings, which were released ahead of
CPHI Barcelona, the world's largest pharma event, held at Fira
Barcelona (October 24-26th, 2023), point to the technology having
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| March 2024
a transformational impact on all parts of drug discovery and
development within the next 24 months.
The CPHI Report features insights from 250 global pharma companies
and is a key barometer of the industry's future growth prospects. For
the first time in the report's history, pharmaceutical 'AI companies' (26%)
have overtaken 'late stage' (20%) and 'early stage' (19%) biotechs as the
industry's most appealing investment option for venture capitals.
Significantly, the rate of change is accelerating, with 62% forecasting
that the first fully AI drug discovered and developed therapy will be
approved by the US FDA within the next five years, and 20% believe
this can be achieved in under two years. By 2030, over half (52%) of
new drugs approved will be discovered or developed using AI.
While there are still no AI-designed drugs in the market, there are a
number of companies across the globe with their drugs in advanced
clinical trials. The first AI-designed drug candidate to enter clinical
trials was reported by UK-based Exscientia in early 2020, a pivotal
moment in AI drug discovery. Since then, several global companies,
including Insilico Medicine, Evotec, and Schrödinger, have announced
phase I trials. Several candidates have had their clinical development
accelerated through AI-enabled solutions.
Insilico Medicine, a US and Hong Kong-based startup, is combining
two rapidly developing technologies, i.e. quantum computing
and generative AI,
to explore lead candidate discovery in drug
development and demonstrate the potential advantages of quantum
generative adversarial networks in generative chemistry.
On the other hand, researchers at the University of Toronto used an
Al-powered protein structure database to uncover a novel treatment
pathway for liver cancer. Apparently, the potential drug was created
in just 30 days!
Quoting another example, we have NVIDIA, a Silicon Valley giant,
recently collaborating with Genentech, a member of the Roche Group,
to accelerate drug discovery using Generative AI. This appears to be
a first-of-its-kind collaboration aiming to optimize each company's
platform and deliver potential medicines to patients faster.
In a new move that underscores the pharmaceutical industry's
commitment to harnessing the power of AI for drug discovery, we
recently saw Sanofi entering into a $140 million multi-year research
collaboration with French pharma tech company Aqemia. Further,
German life sciences firm Merck has launched its first-ever AI solution
to integrate drug discovery and synthesis, combining generative AI,
machine learning and computer-aided drug design.
In addition to these developments, we are also seeing the increasing
participation of non-pharma companies in this space. For instance,
Google Cloud has launched two new AI-powered solutions that
aim to help biotech and pharmaceutical companies accelerate drug
discovery and advance precision medicine; Deloitte has launched an
AI incubator to harness the power of India's tech innovation and talent
capabilities; Accenture has made a strategic investment, through
Accenture Ventures, in QuantHealth, an AI-powered clinical trial
design company that simulates clinical trials in the cloud, allowing
pharmaceutical and biotech companies to develop treatments for
patients more quickly and cost-effectively.

APR March 2024

Table of Contents for the Digital Edition of APR March 2024

Message from the Editor
Editorial Advisory Board
BIOPHARMACEUTICALS - Getting to GMP-Quality Biotherapeutics From Today’s Bench-Scale Continuous Manufacturing Systems: A Gap Analysis
MICROBIOLOGY - Critical Behavioral Attributes and the Application of COM-B Framework in Aseptic Processing
FORMULATION AND DEVELOPMENT - Use of AUC in AAV Analysis in a GMP Setting
FORMULATION AND DEVELOPMENT - Precision Medicine in Clinical Trials: A Statistical Perspective
An Interview with Dan Smithey, PhD President & CEO, Serán
FORMULATION AND DEVELOPMENT - Still Early Days for AI in Drug Discovery...Says Who?
QC Corner - Enhancing Material and Equipment Availability in Production Isolators
BIOPHARMACEUTICALS - Technologies for Aseptic Filling: The Choice is Clear
Vendor Viewpoint - Data Integrity and Rapid Micro Methods: Transforming to a Modern Microbiology Lab
MICROBIOLOGY - Bacterial Spore Formers in Disinfectant Efficacy Testing
Partner Perspective - Nanoparticle Technologies: Enablers for Ocular Drug Delivery
DRUG DELIVERY - Your Nails and You
DRUG DEVELOPMENT - Battling Exorbitance: High Costs in Sickle Cell Gene Therapies and the Imperative of Global Patient Registries for Equity
FORMULATION AND DEVELOPMENT - How Pharma Companies Are Solving Regulatory Challenges with AI-based Technology
Event Preview - CPHI North America
Event Preview - Excipient World 2024
P.I.N. Points
Advertiser's Index
APR March 2024 - CoverTip01
APR March 2024 - CoverTip02
APR March 2024 - Cover1
APR March 2024 - Cover2
APR March 2024 - 1
APR March 2024 - 2
APR March 2024 - 3
APR March 2024 - 4
APR March 2024 - 5
APR March 2024 - Message from the Editor
APR March 2024 - Editorial Advisory Board
APR March 2024 - BIOPHARMACEUTICALS - Getting to GMP-Quality Biotherapeutics From Today’s Bench-Scale Continuous Manufacturing Systems: A Gap Analysis
APR March 2024 - 9
APR March 2024 - 10
APR March 2024 - 11
APR March 2024 - 12
APR March 2024 - 13
APR March 2024 - MICROBIOLOGY - Critical Behavioral Attributes and the Application of COM-B Framework in Aseptic Processing
APR March 2024 - 15
APR March 2024 - 16
APR March 2024 - 17
APR March 2024 - 18
APR March 2024 - FORMULATION AND DEVELOPMENT - Use of AUC in AAV Analysis in a GMP Setting
APR March 2024 - 20
APR March 2024 - 21
APR March 2024 - 22
APR March 2024 - 23
APR March 2024 - FORMULATION AND DEVELOPMENT - Precision Medicine in Clinical Trials: A Statistical Perspective
APR March 2024 - 25
APR March 2024 - 26
APR March 2024 - 27
APR March 2024 - An Interview with Dan Smithey, PhD President & CEO, Serán
APR March 2024 - 29
APR March 2024 - 30
APR March 2024 - FORMULATION AND DEVELOPMENT - Still Early Days for AI in Drug Discovery...Says Who?
APR March 2024 - 32
APR March 2024 - 33
APR March 2024 - QC Corner - Enhancing Material and Equipment Availability in Production Isolators
APR March 2024 - 35
APR March 2024 - BIOPHARMACEUTICALS - Technologies for Aseptic Filling: The Choice is Clear
APR March 2024 - 37
APR March 2024 - 38
APR March 2024 - 39
APR March 2024 - Vendor Viewpoint - Data Integrity and Rapid Micro Methods: Transforming to a Modern Microbiology Lab
APR March 2024 - 41
APR March 2024 - MICROBIOLOGY - Bacterial Spore Formers in Disinfectant Efficacy Testing
APR March 2024 - 43
APR March 2024 - Partner Perspective - Nanoparticle Technologies: Enablers for Ocular Drug Delivery
APR March 2024 - 45
APR March 2024 - 46
APR March 2024 - 47
APR March 2024 - DRUG DELIVERY - Your Nails and You
APR March 2024 - 49
APR March 2024 - 50
APR March 2024 - 51
APR March 2024 - DRUG DEVELOPMENT - Battling Exorbitance: High Costs in Sickle Cell Gene Therapies and the Imperative of Global Patient Registries for Equity
APR March 2024 - 53
APR March 2024 - 54
APR March 2024 - 55
APR March 2024 - FORMULATION AND DEVELOPMENT - How Pharma Companies Are Solving Regulatory Challenges with AI-based Technology
APR March 2024 - 57
APR March 2024 - Event Preview - CPHI North America
APR March 2024 - 59
APR March 2024 - Event Preview - Excipient World 2024
APR March 2024 - 61
APR March 2024 - P.I.N. Points
APR March 2024 - 63
APR March 2024 - Advertiser's Index
APR March 2024 - Cover3
APR March 2024 - Cover4
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