In Silico / Computer-Aided Drug Discovery Services Market: Focus on Large Molecules , 2020-2030
Over time, the complexities of drug discovery, have increased; this is especially true for large molecules, which are inherently more complex than conventional small molecule drugs.
NEW YORK, March 24, 2020 /PRNewswire/ --
INTRODUCTION
The process of drug development, beginning from the discovery of a pharmacological lead to its commercial launch, is estimated to take around 10-15 years, involving capital investments in the range of USD 4-10 billion. Moreover, it is well-known that only a small proportion of leads, which are selected for further investigation during the initial stages of research, are actually translated into product candidates for clinical research studies. Over time, the complexities of drug discovery, have increased; this is especially true for large molecules, which are inherently more complex than conventional small molecule drugs. As a result, there has been a direct rise in overall research and development (R&D) expenditure in the pharmaceutical / biotechnology sector. Specifically, in 2019, the global R&D spending was estimated to be around USD 182 billion, with over 16,000 drug molecules reported to have been investigated. Presently, the industry is currently under tremendous pressure to identify ways to cope with rising capital requirements in drug discovery research and actively avoid losses owing to failed drug development programs. In addition, there has been an evident increase in regulatory stringency, which has made the drug approval process, especially for large molecules, significantly more difficult.
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During the last several years, several computational tools have been developed and introduced for enabling the identification, selection and optimization of pharmacological lead candidates. Currently, there are several in silico approaches available for the drug discovery process alone, such as structure based drug design, fragment based drug discovery and ligand based drug discovery. The predictive power of these in silico tools has been proven to be very advantageous, allowing researchers to bypass the random screening of billions of molecules across hundreds of large molecule targets. According to industry experts, almost 35% of the total cost and time invested in developing a new drug can be saved by adopting an in silico approach. As a result, companies offering in silico drug discovery services, such as computer-aided drug design (CADD), molecular modeling and quantitative structure–activity relationship (QSAR), have now become an important part of the pharmaceutical industry. In future, drug developers, especially those focused on the development of large molecules, are likely to continue relying on outsourcing for a significant part of their respective drug discovery and development operations.
SCOPE OF THE REPORT
The "In Silico / Computer-Aided Drug Discovery Services Market: Focus on Large Molecules (Antibodies, Proteins, Peptides, Nucleic Acid, Gene Therapy and Vectors), 2020-2030 (Including Structure Based Drug Discovery, Fragment Based Drug Discovery, Ligand Based Drug Discovery, Target Based Drug Discovery, Interface Based Drug Discovery Approaches)" report features an extensive study on the current landscape and the likely future potential of the companies offering services for the discovery of large molecule drugs based on the use of in silico tools and techniques. The study features an in-depth analysis, highlighting the capabilities of various industry stakeholders. In addition to other elements, the report includes:
• A detailed review of the overall landscape of companies offering in silico drug discovery services for large molecules, including information on year of establishment, company size, location of headquarters, type of business model used (contract service providers (CROs), software / technology providers, consulting service providers and training service providers), number of drug discovery step(s) for which the company offers services involving the use of in silico approaches (target identification, target validation, hit generation, hit-to-lead and lead optimization), type of large molecules(s) handled (antibodies (monoclonal antibodies, bispecific antibodies, polyclonal antibodies, antibody drug conjugates (ADCs), antibody fragments, single domain antibodies, antisense antibodies and others), proteins (fusion proteins, protein fragments, enzymes and hormones), peptides, cell therapies, gene therapies, vectors and nucleic acids), type of in silico approach used (structure-based drug design (SBDD), fragment-based drug design (FBDD), target-based drug design (TBDD), ligand-based drug design (LBDD) and interface-based drug design (IBDD)), type of in silico service(s) offered (virtual screening, molecular docking, molecular modeling, scaffold hopping and 8+ services), and type of clientele served (pharmaceutical / biotechnology companies and academic / research institutes).
• Insights on contemporary market trends, depicted using four schematic representations, which include [A] a logo landscape of the industry players engaged in this domain, distributed based on the basis of location of their company size (small (1-50 employees), mid-sized (51-200 employees) and large (>200 employees)) and respective headquarters, [B] a tree map representation of in silico service providers, featuring a distribution of stakeholders on the basis of the company size and drug discovery steps, [C] a world map representation, highlighting the key hubs with respect to outsourcing activity within this domain, and [D] an insightful grid analysis, presenting the distribution of companies based on the type of large molecule, in silico approach used and type of clientele.
• Elaborate profiles of key industry players that offer a wide range of in silico drug discovery services, featuring a brief overview of the company (including details related to year of establishment, company size, location of headquarters and key members of the executive team), funding and investment information (if available), in silico-based service(s) portfolio) and an informed future outlook.
• A detailed peer group-based benchmarking analysis, comparing the involved players based on several relevant parameters, such as the experience of the company, number of drug discovery step(s), number of in silico service(s) offered, number of large molecule(s) for which the aforementioned services are offered and type of clientele.
• An insightful competitiveness analysis featuring a four-dimensional bubble chart, highlighting the key players in this domain on the basis of the strength of their respective service portfolios, taking into consideration the experience of a service provider, number of drug discovery services offered and number of large molecules, for which the aforementioned services are offered.
• A detailed analysis assessing the current opportunity within in silico drug discovery services market, comparing the number of pipeline products and current market size across different types of large molecules, and the availability and capabilities of affiliated in silico drug discovery service providers.
• A discussion on the various business strategies that can be adopted by in silico drug discovery service providers in order to maintain a competitive edge in this industry, based on the different types of large molecules handled and the technical expertise of service providers, in terms of capabilities across different steps of drug discovery.
• An insightful analysis highlighting the cost saving potential associated with the use of in silico approaches in the drug discovery process.
• A case study comparing the key challenges associated with the discovery and production of large molecules, affiliated product development timelines, and manufacturing protocols, with those of small molecule drugs.
• Insights from an industry-wide survey, featuring inputs solicited from various experts who are directly / indirectly involved in providing in silico services for discovery of large molecule drugs.
• A discussion on the upcoming computational approaches (such as artificial intelligence and cloud computing) that are being adopted for drug discovery purposes and are likely to impact early stage research over the coming years.
• A case study highlighting several non-computational methods / technologies, which are considered to be of significant importance to the overall drug discovery process.
One of the key objectives of this report was to understand the primary growth drivers and estimate the future size of the in silico drug discovery services market. Based on several parameters, such as number of large molecules based drug discovery projects, adoption of in silico services for drug discovery and outsourcing profile, we have provided informed estimates on the likely evolution of the market for the period 2020-2030. The report also provides details on the likely distribution of the current and forecasted opportunity across [A] key step(s) of drug discovery (target identification, target validation, hit generation, hit-to-lead and lead optimization), [B] type of large molecule (antibodies, proteins, peptides, nucleic acids and vectors), [C] company size (small, mid-sized and large), [D] therapeutic area (autoimmune disorders, blood disorders, cardiovascular disorders, gastrointestinal and digestive disorders, hormonal disorders, HIV / AIDS, infectious diseases, metabolic disorders, mental disorders, musculoskeletal disorders, neurological disorders, oncological disorders, respiratory disorders, skin disorders, urogenital disorders and others), [E] type of clientele (pharmaceutical / biotechnology companies and academic / research institutes), and [F] key geographical regions (North America (the US and Canada), Europe (Italy, Germany, France, Spain and rest of Europe) and Asia-Pacific (China, India and Japan), along with the rest of the world). In order to account for future uncertainties and to add robustness to our model, we have provided three forecast scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry's growth.
The opinions and insights presented in this report were also influenced by inputs solicited via a survey and discussions held with senior stakeholders in the industry. The report features detailed transcripts of discussions held with the following individuals (in alphabetical order):
• John L Kulp (Chief Executive Officer and Chief Technical Officer, Conifer Point)
• Sven Benson (Founder, candidum)
• Mark Whittaker (Senior Vice President, Evotec)
• Edelmiro Moman (Scientific Consultant and Teacher, ProSciens)
All actual figures have been sourced and analyzed from publicly available information forums and primary research discussions. Financial figures mentioned in this report are in USD, unless otherwise specified.
RESEARCH METHODOLOGY
The data presented in this report has been gathered via secondary and primary research. For all our projects, we conduct interviews with experts in the area (academia, industry, medical practice and other associations) to solicit their opinions on emerging trends in the market. This is primarily useful for us to draw out our own opinion on how the market will evolve across different regions and technology segments. Where possible, the available data has been checked for accuracy from multiple sources of information.
The secondary sources of information include:
• Annual reports
• Investor presentations
• SEC filings
• Industry databases
• News releases from company websites
• Government policy documents
• Industry analysts' views
While the focus has been on forecasting the market till 2030, the report also provides our independent view on technological and non-commercial trends emerging in the industry. This opinion is solely based on our knowledge, research and understanding of the relevant market gathered from various secondary and primary sources of information.
CHAPTER OUTLINES
Chapter 2 is an executive summary of the key insights captured in our research. It offers a high-level view on the current state of the in silico services market and its likely evolution in the short-mid term and long term.
Chapter 3 provides an introduction to the overall drug discovery process, including details on the time taken for a drug to traverse from the bench to the market, and the various stages of the drug discovery process. It also provides an overview of various in silico drug discovery approaches, and a detailed classification of computer-aided research tools and techniques. In addition, it features information on the applications of in silico tools across different steps of the drug discovery process, along with details of specific in silico methods / approaches associated with these steps. It also highlights the benefits of the in silico approach and the key challenges associated with carrying out in silico (including CADD) drug discovery research in-house. Finally, the chapter highlights the current preference to outsource such operations, especially for large molecule drugs.
Chapter 4 provides an assessment of the global landscape of the in silico services market. It includes information on over 60 players that are currently engaged in providing such services for discovery of large molecules. It features an in-depth market overview, including information on location of headquarters, employee count, type of business model used (contract service providers, software / technology providers, consulting service providers and training service providers), number of drug discovery step(s) for which the company offers services involving the use of in silico approaches (target identification, target validation, hit generation, hit-to-lead and lead optimization), type of large molecules(s) handled (antibodies (monoclonal antibodies, bispecific antibodies, polyclonal antibodies, ADCs, antibody fragments, single domain antibodies, antisense antibodies and others), proteins (fusion proteins, protein fragments, enzymes and hormones), peptides, cell therapies, gene therapies, vectors, nucleic acids), type of in silico approach used (SBDD, FBDD, TBDD, LBDD and IBDD), type of in silico service(s) offered (virtual screening, molecular docking, molecular modeling, scaffold hopping and 8+ services), and type of clientele served (pharmaceutical / biotechnology companies and academic / research institutes). It also includes the list of various software tools / web applications / technology platforms offered by the in silico service providers for the drug discovery, has been provided.
Chapter 5 presents a compilation of the key insights generated from the study highlighting the contemporary market trends, depicted using four schematic representations, which include [A] a logo landscape of the industry players engaged in this domain, distributed based on the basis of the location of their company size (small-sized (1-50 employees), mid-sized (51-200 employees) and large (>200 employees)) and respective headquarters, [B] a tree map representation of in silico service providers, featuring a distribution of stakeholders on the basis of the company size and drug discovery steps, [C] a world map representation, highlighting the key hubs with respect to outsourcing activity within this domain, and [D] an insightful grid analysis, presenting the distribution of companies based on the type of large molecule, in silico approach used and type of clientele.
Chapter 6 features detailed profiles of key industry in silico drug discovery service providers, which were established after 2000 and have an employee base of more than 500 individuals. Each profile provides an overview of the company (including details related to year of establishment, company size, location of headquarters and key members of the executive team), funding and investment information (if available), in silico-based service(s) portfolio, and an informed future outlook. In addition, each profile features a peer group-based benchmark comparison matrix for the players based on several parameters, such as experience of the company, number of drug discovery step(s), number of in silico service(s) offered, number of large molecule(s) for which the aforementioned services are offered and type of clientele.
Chapter 7 features an insightful competitiveness analysis featuring a four-dimensional bubble chart, highlighting the key players in this domain on the basis of the strength of their respective service portfolios, taking into consideration the experience of a service provider, number of drug discovery services and number of large molecules for which the aforementioned services are offered.
Chapter 8 presents the detailed analysis assessing the current opportunity within in silico drug discovery services market, along with the comparison of the number of pipeline products and current market size across different types of large molecules and the availability and capabilities of affiliated in silico drug discovery service providers.
Chapter 9 features the various business strategies that can be adopted by in silico service providers for maintaining the competitive edge in the industry, based on the different types of large molecules handled and the technical expertise of service providers, in terms of capabilities across different steps of drug discovery.
Chapter 10 presents the comparison of the challenges associated with the discovery and production of large molecules, comparing affiliated product development timelines, and manufacturing protocols with those of small molecule drugs. In addition, several approaches have been highlighted, which can be incorporated to overcome the challenges in the drug discovery of large molecules.
Chapter 11 presents insights generated from an industry-wide survey, wherein we had approached nearly 100 stakeholders involved in outsourcing in silico services. The participants, who were primarily Directors / CXO level representatives of their respective companies, helped us develop a deeper understanding on the nature of their services and their associated commercial potential.
Chapter 12 presents a comprehensive analysis of the cost saving potential associated with the use of in silico services in the drug discovery process for large molecules.
Chapter 13 presents a comprehensive market forecast analysis, highlighting the future potential of the in silico drug discovery services market till the year 2030. It features the likely distribution of the market based on [A] key step(s) of drug discovery (target identification, target validation, hit generation, hit-to-lead and lead optimization), [B] type of large molecule (antibodies, proteins, peptides, nucleic acids and vectors), [C] company size (small-sized, mid-sized and large), [D] therapeutic areas (autoimmune disorders, blood disorders, cardiovascular disorders, gastrointestinal and digestive disorders, hormonal disorders, HIV / AIDS, infectious diseases, metabolic disorders, mental disorders, musculoskeletal disorders, neurological disorders, oncological disorders, respiratory disorders, skin disorders, urogenital disorders and others), [E] type of clientele (pharmaceutical / biotechnology companies and academic / research institutes), and [F] key geographical regions (North America (the US and Canada), Europe (Italy, Germany, France, Spain and rest of Europe), and Asia-Pacific (China, India and Japan), along with the rest of the world). In order to account for future uncertainties and to add robustness to our model, we have provided three forecast scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry's growth.
Chapter 14 provides a brief overview of the upcoming computational technologies, which are being developed to deal with the innate complexities associated with the process of drug discovery and optimize the overall time spent on early stage research.
Chapter 15 is a collection of interview transcripts of discussions held with key stakeholders in this industry. In this chapter, we have presented the details of our conversations held with John L Kulp (Chief Executive Officer and Chief Technical Officer, Conifer Point Pharmaceuticals), Sven Benson (Founder, candidum), Mark Whittaker (Senior Vice President, Evotec) and Edelmiro Moman (Scientific Consultant and Teacher, ProSciens).
Chapter 16 is an appendix, which provides tabulated data and numbers for all the figures provided in the report.
Chapter 17 is an appendix, which provides the list of companies and organizations mentioned in the report.
Chapter 18 is an appendix, which features a detailed discussion of several non-computational methods / technologies, which are considered to be of significant importance to the overall drug discovery process.
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