Drug Development Paradigm Research Articles

Project Optimus: An Overview of the Principles and Challenges

Phase 1 trial designs to establish the appropriate dose for cytotoxic agents are based on the assumption that both clinical benefit & toxicity increase with dose. These studies seek to establish the maximum tolerated dose (MTD) for future development, for targeted non-cytotoxic therapies maximum efficacy may be achieved at doses below the MTD. The FDA has set up Project Optimus (PO) to reform the dose optimisation and dose selection paradigm for cancer drug development. PO is a bid for balance: maintaining treatment efficacy at a therapeutic dose that does not generate toxicities that could otherwise be avoided with a different dose. PO guidance includes that dose escalation decisions in Phase I trials should consider preclinical data (ideally using models that predict human efficacy, toxicity, and receptor engagement), toxicity (including early and delayed, low-grade toxicities, and patient-reported outcomes), pharmacokinetic (PK) data, efficacy data and pharmacodynamic (PD) data. Phase I studies should identify a dose range within which efficacy has been observed rather than a single dose for further development. Adherence to PO principles will have a large impact on early oncology development. This presentation summarises the key PO guidance and the challenges that ensue.

Building on the successes of patient-focused drug development: a call for new policies to maintain momentum.

Since its commencement as part of the Food and Drug Administration's (FDA) Prescription Drug User Fee Act (PDUFA) V in 2012, patient-focused drug development (PFDD) has become an integral part of the drug development paradigm. FDA encourages the development and use of Patient-Experience Data (PED) as it provides important information on the patients' needs and perspectives and inform regulatory decision-making. While the FDA is required to fill out a table which includes a list of various types of Patient Experience Data (PED) and if such data was reviewed by FDA as part of a drug application, there is still a need to understand how FDA uses PED in its regulatory decision-making. This article examines whether new policies are needed to ensure the full potential of PFDD.

Oncology Combination Drug Development Strategies for Project Optimus.

The Project Optimus initiative from the FDA introduced a new dose optimization and selection paradigm in oncology drug development. The FDA has outlined approaches to dose optimization for single agents, but multiple oncology drugs are being developed for use in combination with other therapies. Dose optimization in the context of combination drug development is complex and requires a case-by-case approach. It necessitates commitment to the totality of available evidence, leveraging all relevant data on mechanism of action, nonclinical and clinical pharmacology, safety, and principles of model-informed drug development. In this article, we outline key considerations for sponsors and investigators pursuing dose optimization with combinatorial regimens. We illustrate important strategies for dose optimization in the combination setting using a range of hypothetical case examples that represent typical drug development scenarios. Close discussions and collaboration with regulators regarding the optimal approaches to these scenarios will continue to be critical.

AI-powered omics-based drug pair discovery for pyroptosis therapy targeting triple-negative breast cancer

Due to low success rates and long cycles of traditional drug development, the clinical tendency is to apply omics techniques to reveal patient-level disease characteristics and individualized responses to treatment. However, the heterogeneous form of data and uneven distribution of targets make drug discovery and precision medicine a non-trivial task. This study takes pyroptosis therapy for triple-negative breast cancer (TNBC) as a paradigm and uses data mining of a large TNBC cohort and drug databases to establish a biofactor-regulated neural network for rapidly screening and optimizing compound pyroptosis drug pairs. Subsequently, biomimetic nanococrystals are prepared using the preferred combination of mitoxantrone and gambogic acid for rational drug delivery. The unique mechanism of obtained nanococrystals regulating pyroptosis genes through ribosomal stress and triggering pyroptosis cascade immune effects are revealed in TNBC models. In this work, a target omics-based intelligent compound drug discovery framework explores an innovative drug development paradigm, which repurposes existing drugs and enables precise treatment of refractory diseases.

Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities.

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Evolution of the Innovative Therapies for Children With Cancer Consortium Trial Portfolio for Drug Development for Children With Cancer.

The aim of the Innovative Therapies for Children with Cancer (ITCC) consortium is to improve access to novel therapies for children and adolescents with cancer. The evolution of the ITCC clinical trial portfolio since 2003 was reviewed. All ITCC-labeled phase I/II trials opened between January 1, 2003 and February 3, 2018 were analyzed in two periods (2003-2010 and 2011-2018), and data were extracted from the ITCC database, regulatory agencies' registries, and publications. Sixty-one trials (62% industry-sponsored) enrolled 3,198 patients. The number of trials in the second period increased by almost 300% (16 v 45). All biomarker-driven trials (n = 14) were conducted in the second period. The use of rolling six and model-based designs increased (1 of 9, 11% v 21 of 31, 68%), and that of 3 + 3 designs decreased (5 of 9, 55% v 5 of 31, 16%; P = .014). The proportion of studies evaluating chemotherapeutics only decreased (5 of 16, 31% v 4 of 45, 9%), the proportion of single-agent targeted therapies did not change (9 of 16, 56.2% v 24 of 45, 53.3%), the proportion of combination targeted therapies trials increased (2 of 16, 12%, v 17 of 45, 38%), the proportion of randomized phase II trials increased (1 of 7, 14% v 8 of 14, 57%). More trials were part of a pediatric investigation plan in the second period (4 of 16, 25% v 21 of 45, 46%). The median time for Ethics Committees' approvals was 1.7 times longer for academic compared with industry-sponsored trials. This study reports a shift in the paradigm of early drug development for childhood cancers, with more biologically relevant targets evaluated in biomarker-driven trials or in combination with other therapies and with more model-based or randomized designs and a greater focus on fulfilling regulatory requirements. Improvement of trial setup and recruitment could increase the number of patients benefiting from novel agents.

A Bayesian latent-subgroup platform design for dose optimization.

The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.

MT-Keyboard: A Bayesian Model-Assisted Interval Design to Account for Toxicity Grades and Types for Phase I Trials

Conventionally, dose finding trials are based on dose-limiting toxicity (DLT) that only captures the most severe toxicities, for example, treatment related grade 3 or higher toxicity according to the NCI Common Terminology Criteria for Adverse Events. However, this approach is often problematic for certain novel targeted therapies and immunotherapies, which may not induce DLT within a clinically active dose range and are often characterized by low grade toxicities. This important issue has been highlighted and discussed in the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section Open Forums, and is also an important consideration of the Project Optimus initiated by FDA to “reform the dose optimization and dose selection paradigm in oncology drug development.” In this article, we propose an easy-to-implement model-assisted Bayesian design, known as multiple toxicity keyboard (MT-Keyboard) design, to incorporate toxicity grades and types into dose finding. The MT-Keyboard design is able to accommodate binary, quasi-binary and continuous toxicity endpoints that are constructed to account for toxicity grades and types. We further extend the MT-Keyboard design, referred to as TITE-MT-Keyboard, to accommodate late-onset toxicity using the approximated likelihood approach. Simulation shows that the MT-Keyboard and TITE-MT-Keyboard designs have desirable operating characteristics, comparable to or better than some existing designs. A web-based software to implement the design will be freely available at www.trialdesign.org. Supplementary materials for this article are available online.

A self‐reinforced activatable photosensitizer prodrug enabling synergistic photodynamic and chemotherapy

AbstractAs a novel drug development paradigm, selective activation of prodrugs provides the potential for precise tumor chemotherapy, thereby presenting an opportunity for advancing cancer treatment. The combination of photodynamic therapy (PDT) and prodrug can enhance the therapeutic efficacy while simultaneously enabling real‐time monitoring of drug distribution and release. However, the tumor hypoxia microenvironment and the frequent high‐dose administration of prodrugs significantly impede therapeutic efficacy and escalate treatment‐related risks. Herein, a tumor microenvironment‐specific release prodrug is constructed, termed NBS‐2S‐5FU. Under the influence of glutathione (GSH), NBS‐2S‐5FU undergoes activation, leading to the release of photosensitizer NBS and chemotherapeutic agent 5‐FU derivatives. Under irradiation, NBS produces sufficient superoxide radical () while 5‐FU derivatives inhibit DNA biosynthesis, thereby effectively suppressing tumor growth at low doses. Subsequent in vivo studies utilizing NBS‐2S‐5FU liposomes exhibit outstanding anti‐cancer effectiveness. This study highlights a promising direction for advancing combined prodrugs that integrate PDT and chemotherapy.

A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy.

While large-scale artificial intelligence (AI) models for protein structure prediction and design are advancing rapidly, the translation of deep learning models for practical macromolecular drug development remains limited. This investigation aims to bridge this gap by combining cutting-edge methodologies to create a novel peptide-based PROTAC drug development paradigm. Using ProteinMPNN and RFdiffusion, we identified binding peptides for androgen receptor (AR) and Von Hippel-Lindau (VHL), followed by computational modeling with Alphafold2-multimer and ZDOCK to predict spatial interrelationships. Experimental validation confirmed the designed peptide's binding ability to AR and VHL. Transdermal microneedle patching technology was seamlessly integrated for the peptide PROTAC drug delivery in androgenic alopecia treatment. In summary, our approach provides a generic method for generating peptide PROTACs and offers a practical application for designing potential therapeutic drugs for androgenetic alopecia. This showcases the potential of interdisciplinary approaches in advancing drug development and personalized medicine.

Experience on Utilization of Novel Modeling & Simulation Approaches in Generic Product Development

Modeling and simulation approaches such as Physiologically Based Pharmacokinetic (PBPK) and Physiologically Based Biopharmaceutics Modeling (PBBM) are gaining importance due to their impact to transform drug development paradigm. Development of such modeling approaches include integration of drug substance, drug product and physiological aspects followed by validation against clinical data. In this manuscript, we have highlighted utility of PBPK and PBBM in generic product development using case examples. An application where PBBM was utilized to obtain biowaivers and dissolution safe space was portrayed. Utility of such modeling approaches in obtaining fed bioequivalence studies was discussed with case example. Impact of gender evaluation on bioequivalence study outcome using PBPK approach was highlighted with a case example. Evaluation of safety aspects of extended release formulation with help of modeling approach was discussed with practical example. In the last case study, understanding the impact of critical bioavailability attributes with help of PBBM was indicated. Further, future direction in terms of sharing best practices across academia, industry and regulatory was highlighted. Overall, this manuscript summarizes experience on utilization of modeling and simulation approaches in generic product development with an aim to have more affordable and quality medicines to the patients.

Antitumor activity of Z15-0-2, a bispecific nanobody targeting PD-1 and CTLA-4

The combination of programmed cell death protein 1 (PD-1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) antibodies has potential for enhancing clinical efficacy. We described the development and antitumor activity of Z15-0, a bispecific nanobody targeting both the PD-1 and CTLA-4 pathways simultaneously. We designed and optimized the mRNA sequence encoding Z15-0, referred to as Z15-0-2 and through a series of in vitro and in vivo experiments, we established that the optimized Z15-0-2 mRNA sequence significantly increased the expression of the bispecific nanobody. Administration of Z15-0-2 mRNA to tumor-bearing mice led to greater inhibition of tumor growth compared to controls. In aggregate, we introduced a novel bispecific nanobody and have re-engineered it to boost expression of mRNA, representing a new drug development paradigm.

Design Strategy and Consideration for Oncology Dose-Optimization: An Industry Perspective

The conventional more-is-better dose selection paradigm is often inappropriate for novel molecularly targeted agents. In response, the US Food and Drug Administration (FDA) initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. Furthermore, the FDA recently also published draft guidance “Optimizing the dosage of human prescription drugs and biological products for the treatment of oncologic diseases guidance for industry,” providing overviews on the rationale, importance, and principles of dose optimization from the regulatory perspective. However, detailed guidance on how to design dose optimization trials is still lacking; it is a key issue faced by drug developers. The article discusses statistical and design strategies of dose optimization in oncology by sharing current industry practice on dose-finding, delineating key statistical components for dose optimization trials, describing available design strategies, and providing recommendations and future directions. This article is a joint work of the Statistical Methods in Oncology Scientific Working Group (Dose-Optimization Workstream) of the American Statistical Association Biopharmaceutical Section.

Drug discovery and development in the era of artificial intelligence: From machine learning to large language models

Drug discovery and development in the era of artificial intelligence: From machine learning to large language models

PHARMACEUTICAL INDUSTRY ANALYSIS: A COMPREHENSIVE REVIEW OF MARKET TRENDS, COMPETITIVE LANDSCAPE, AND FUTURE OUTLOOK

The pharmaceutical industry, a cornerstone of modern healthcare, undergoes constant evolution to meet the dynamic demands of the healthcare landscape. This comprehensive review delves into the multifaceted dimensions of the industry, encompassing market trends, competitive dynamics, and future projections. Market trends reveal a shift towards personalized medicine, leveraging advancements in genomics and biotechnology, alongside a growing focus on specialty drugs catering to niche patient populations. The prevalence of chronic diseases and an aging populace accentuate the necessity for innovative therapies, while the digital revolution introduces new paradigms in patient care and drug development, propelled by telemedicine, digital therapeutics, and artificial intelligence. Simultaneously, regulatory pressures and pricing constraints exert significant influence, necessitating adaptability among industry players. In the competitive landscape, established pharmaceutical giants vie for market dominance amidst patent expirations and generic competition, while biotech startups and niche players disrupt traditional models. Mergers and acquisitions serve as strategic imperatives for pipeline fortification and market expansion. Looking forward, the industry must navigate a terrain characterized by rapid technological advancements, evolving patient needs, and regulatory complexities, all while striving for innovation, accessibility, and sustainability in the delivery of healthcare solutions. Keywords: Pharmaceutical industry, healthcare landscape, market trends, personalized medicine, specialty drugs, chronic diseases.

Drug Classification and Repurposing Using Decision Tree Algorithm and Data Analysis

Abstract: Fundamentally, drugs are substances that exhibit the capacity to alter biological functions, often serving as interventions to mitigate ailments or enhance physiological processes. However, the landscape of drug development and usage is undergoing transformation, fueled by innovative strategies that hold the promise of revolutionizing healthcare solutions. This research paper explores drug classification and repurposing through the utilization of decision tree algorithms and data analysis. Specifically, the aim is to classify drugs into three distinct types: Drug X which is most used drugs, Drug Y which is less frequently used drugs, and Drug C includes drugs like narcotics. Decision tree algorithms are employed to discern the defining attributes that categorize drugs into these types. By analyzing comprehensive datasets encompassing drug properties, interactions, and clinical outcomes, the study harnesses decision tree models to predict drug classifications accurately. This approach holds the potential to accelerate drug discovery, optimize treatment strategies, and contribute to more efficient healthcare solutions. The proposed algorithm is compared with both Naïve Bayes and K - Nearest Neighbors algorithms to prove it is more accurate. Ultimately, the significance of this paper transcends the boundaries of conventional drug development paradigms and to overcome the problem of shortage of drugs.

Early, precise, and safe clinical evaluation of the pharmacodynamic effects of novel agents in the intact human tumor microenvironment.

Introduction: Drug development is systemically inefficient. Research and development costs for novel therapeutics average hundreds of millions to billions of dollars, with the overall likelihood of approval estimated to be as low as 6.7% for oncology drugs. Over half of these failures are due to a lack of drug efficacy. This pervasive and repeated low rate of success exemplifies how preclinical models fail to adequately replicate the complexity and heterogeneity of human cancer. Therefore, new methods of evaluation, early in the development trajectory, are essential both to rule-in and rule-out novel agents with more rigor and speed, but also to spare clinical trial patients from the potentially toxic sequelae (high risk) of testing investigational agents that have a low likelihood of producing a response (low benefit). Methods: The clinical in vivo oncology (CIVO®) platform was designed to change this drug development paradigm. CIVO precisely delivers microdose quantities of up to 8 drugs or combinations directly into patient tumors 4-96h prior to planned surgical resection. Resected tissue is then analyzed for responses at each site of intratumoral drug exposure. Results: To date, CIVO has been used safely in 6 clinical trials, including 68 subjects, with 5 investigational and 17 approved agents. Resected tissues were analyzed initially using immunohistochemistry and in situ hybridization assays (115 biomarkers). As technology advanced, the platform was paired with spatial biology analysis platforms, to successfully track anti-neoplastic and immune-modulating activity of the injected agents in the intact tumor microenvironment. Discussion: Herein we provide a report of the use of CIVO technology in patients, a depiction of the robust analysis methods enabled by this platform, and a description of the operational and regulatory mechanisms used to deploy this approach in synergistic partnership with pharmaceutical partners. We further detail how use of the CIVO platform is a clinically safe and scientifically precise alternative or complement to preclinical efficacy modeling, with outputs that inform, streamline, and de-risk drug development.

Equivariant 3D-conditional diffusion model for molecular linker design

AbstractFragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fragments of interest to obtain chemically relevant candidate drug molecules. In this work, we propose DiffLinker, an E(3)-equivariant three-dimensional conditional diffusion model for molecular linker design. Given a set of disconnected fragments, our model places missing atoms in between and designs a molecule incorporating all the initial fragments. Unlike previous approaches that are only able to connect pairs of molecular fragments, our method can link an arbitrary number of fragments. Additionally, the model automatically determines the number of atoms in the linker and its attachment points to the input fragments. We demonstrate that DiffLinker outperforms other methods on the standard datasets, generating more diverse and synthetically accessible molecules. We experimentally test our method in real-world applications, showing that it can successfully generate valid linkers conditioned on target protein pockets.

Current issues in dose-finding designs: A response to the US Food and Drug Adminstration's Oncology Center of Excellence Project Optimus.

With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration's Oncology Center of Excellence initiated Project Optimus, with the goal "to reform the dose optimization and dose selection paradigm in oncology drug development." As a response to Project Optimus, the articles in this special issue of Clinical Trials review recent advances in methods for choosing the dose or schedule of a new agent with an overall objective of informing clinical trialists of these innovative designs. This introductory article briefly reviews problems with conventional methods, the regulatory changes that encourage better dose optimization designs, and provides brief summaries of the articles that follow in this special issue.

Targeting KRAS in cancer.

RAS family variants-most of which involve KRAS-are the most commonly occurring hotspot mutations in human cancers and are associated with a poor prognosis. For almost four decades, KRAS has been considered undruggable, in part due to its structure, which lacks small-molecule binding sites. But recent developments in bioengineering, organic chemistry and related fields have provided the infrastructure to make direct KRAS targeting possible. The first successes occurred with allele-specific targeting of KRAS p.Gly12Cys (G12C) in non-small cell lung cancer, resulting in regulatory approval of two agents-sotorasib and adagrasib. Inhibitors targeting other variants beyond G12C have shown preliminary antitumor activity in highly refractory malignancies such as pancreatic cancer. Herein, we outline RAS pathobiology with a focus on KRAS, illustrate therapeutic approaches across a variety of malignancies, including emphasis on the 'on' and 'off' switch allele-specific and 'pan' RAS inhibitors, and review immunotherapeutic and other key combination RAS targeting strategies. We summarize mechanistic understanding of de novo and acquired resistance, review combination approaches, emerging technologies and drug development paradigms and outline a blueprint for the future of KRAS therapeutics with anticipated profound clinical impact.