Our recent publications

https://www.bioprocessonline.com/doc/using-ai-to-predict-multispecific-formulation-patterns-0001

 Bispecific and trispecific antibodies are engineered to bind two or more distinct antigens or epitopes, enabling novel therapeutic mechanisms such as T cell redirection, dual immune checkpoint blockade, and multispecific viral neutralization. Their promise has driven rapid clinical growth: as of March 2025, over 600 bispecific antibodies are in clinical trials, with 17 FDA-approved products and a projected market exceeding $50 billion by 2030.1,2 Additionally, more than 50 trispecific antibodies are in clinical development, with first approvals anticipated by 2028.3 The U.S. and China dominate bispecific and trispecific antibody research. 

https://www.bioprocessonline.com/doc/ai-s-potential-to-aid-multispecific-analytical-characterization-0001

 While AI tools can predict formulation parameters, their recommendations must be experimentally validated through robust analytical characterization. The following section outlines key analytical methods essential for assessing stability, aggregation, and manufacturability of multispecific antibodies. 

Outsourced Pharma, June 2025, in-press

In biologics and cell and gene therapy (CGT), selecting and managing Contract Development and Manufacturing Organizations (CDMOs) has become a strategic imperative—but traditional approaches are struggling to keep up. Manual workflows, siloed data, and fragmented oversight slow development and increase risk. As advanced therapies grow more complex and global, inefficiencies in CDMO operations can lead to costly delays and compliance failures. Artificial intelligence (AI) is now reshaping this landscape. By integrating predictive analytics, NLP, and robotics, AI enables faster selection, smarter oversight, and proactive risk management, offering a more agile and resilient framework for modern CDMO partnerships.

BioProcess International, July 2025, In-press

Artificial intelligence (AI) is redefining pharmaceutical Quality Management Systems (QMS) by enhancing process efficiency, regulatory compliance, and risk mitigation. This article explores how AI technologies, including machine learning, natural language processing, and predictive analytics, are being applied across key QMS functions: deviation and CAPA management, change control, document control and compliance, risk management, and inspection readiness. Through detailed case studies and real-world examples, the paper demonstrates how AI shifts quality operations from reactive to proactive, offering predictive insights, automating manual processes, and strengthening inspection preparedness. The discussion also reviews leading AI platforms and tools used in the industry, outlining strategic considerations for implementation. The vision of AI in QMS is presented as a pathway toward continuous improvement, enhanced data integrity, and smarter compliance management.

In-press, July 2025

In biopharmaceutical manufacturing, increasing demands for data integrity, process automation, and regulatory transparency are driving the adoption of digital reference materials (dRM), also known as digital reference standards (DRS). These structured, machine-readable datasets represent the digital equivalent of traditional physical reference standards used in quality control (QC). Unlike paper-based Certificates of Analysis (CoAs), which require manual transcription, dRMs integrate seamlessly with laboratory information systems (e.g., LIMS, ELNs, CDS) and enable automated workflows, reducing errors and enhancing compliance.

In-press August 2025

The pharmaceutical industry is undergoing a profound digital transformation, driven by the convergence of artificial intelligence (AI), data science, and regulatory modernization. At the heart of this transformation is the growing interest in Digital Reference Materials (dRM), structured, machine-readable representations of traditional reference standards used to ensure the quality, identity, and purity of drug substances and products. While current dRM implementations primarily focus on digitizing Certificates of Analysis (CoAs) and analytical data using formats such as XML, JSON, or AnIML, the next frontier lies in enhancing their functionality and intelligence using advanced AI technologies.

In-press August 2025

Lyme disease, caused by the bacterium Borrelia burgdorferi, is the most common vector-borne illness in the United States and Europe. While early-stage Lyme disease can often be successfully treated with antibiotics such as doxycycline, amoxicillin, or ceftriaxone, a substantial subset of patients continues to experience persistent, debilitating symptoms. These long-term manifestations are often labeled under umbrella terms such as chronic Lyme disease (CLD), post-treatment Lyme disease syndrome (PTLDS), or Lyme infection-associated chronic illnesses (IACI), though these terms are frequently used interchangeably in both professional medical literature and popular publications, they refer to distinct clinical situations.