Achieving end-to-end success in the clinic: Pfizer's learnings on R&D productivity

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Successes and pitfalls in orphan drug development for sickle cell disease

ABSTRACT

Sickle cell disease (SCD) is a hereditary red cell disorder with a large disease burden at a global level. In the United States and Europe, medicines may qualify for orphan designation (OD), a regulatory status that provides incentives to boost development. We evaluated the development of new therapies for SCD using data for OD granted in the United States and Europe over the last 2 decades (2000-2021). We analyzed their characteristics, pathophysiological targets, trends, and OD sponsors. We then investigated the approval outcomes, including the phase success rate and reasons for discontinuation across different variables. We identified 57 ODs for SCD: 43 (75.4%) small molecules, 32 (56.1%) for oral administration, and 36 (63.1%) for chronic use to prevent SCD complications. At the end of the study (2021), development of 34 of 57 ODs was completed. Four ODs were approved with a success rate of 11.8%. Products targeting upstream causative events of SCD pathophysiology had a 1.8 higher success rate compared with products targeting disease consequences. Large companies showed a fourfold higher success rate compared with small-medium enterprises. Failures in clinical development were mainly seen in phase 3 for a lack of efficacy on vaso-occlusive crisis as the primary study end point, likely related to variable definitions and heterogeneity of pain scoring and treatment. Both advances in SCD knowledge and regulatory incentives paved the way for new therapies for SCD. Our finding of high failure rates in late-stage clinical development signals the need for better early-stage predictive models, also in the context of meaningful clinical end points.

Frameworks for transformational breakthroughs in RNA-based medicines

RNA has sparked a revolution in modern medicine, with the potential to transform the way we treat diseases. Recent regulatory approvals, hundreds of new clinical trials, the emergence of CRISPR gene editing, and the effectiveness of mRNA vaccines in dramatic response to the COVID-19 pandemic have converged to create tremendous momentum and expectation. However, challenges with this relatively new class of drugs persist and require specialized knowledge and expertise to overcome. This Review explores shared strategies for developing RNA drug platforms, including layering technologies, addressing common biases and identifying gaps in understanding. It discusses the potential of RNA-based therapeutics to transform medicine, as well as the challenges associated with improving applicability, efficacy and safety profiles. Insights gained from RNA modalities such as antisense oligonucleotides (ASOs) and small interfering RNAs are used to identify important next steps for mRNA and gene editing technologies.

Master protocols and other innovative trial designs in inflammation and immunology to expedite clinical drug development

Master protocol designs, such as umbrella and basket studies, allow multiple compounds or multiple target populations to be evaluated simultaneously within a single protocol, and have been widely adopted in oncology clinical trials. These novel designs can also be applied in other therapeutic areas, where they could have several benefits over conducting traditional randomized controlled trials. Here, we detail Pfizer's recent implementations of master protocol designs in inflammation and immunology clinical studies, focusing on the opportunities for cost and resource savings and how these designs can expedite the time required to bring new treatments to patients in need.

Accelerating drug development at Bristol Myers Squibb through innovation

This paper focuses on the use of novel technologies and innovative trial designs to accelerate evidence generation and increase pharmaceutical Research and Development (R&D) productivity, at Bristol Myers Squibb. We summarize learnings with case examples, on how we prepared and continuously evolved to address the increasing cost, complexities, and external pressures in drug development, to bring innovative medicines to patients much faster. These learnings were based on review of internal efforts toward accelerating R&D focusing on four key areas: adopting innovative trial designs, optimizing trial designs, leveraging external control data, and implementing novel methods using artificial intelligence and machine learning.

Adult brain tumour research in 2024: Status, challenges and recommendations

In 2015, a groundswell of brain tumour patient, carer and charity activism compelled the UK Minister for Life Sciences to form a brain tumour research task and finish group. This resulted, in 2018, with the UK government pledging £20m of funding, to be paralleled with £25m from Cancer Research UK, specifically for neuro-oncology research over the subsequent 5 years. Herein, we review if and how the adult brain tumour research landscape in the United Kingdom has changed over that time and what challenges and bottlenecks remain. We have identified seven universal brain tumour research priorities and three cross-cutting themes, which span the research spectrum from bench to bedside and back again. We discuss the status, challenges and recommendations for each one, specific to the United Kingdom.

Comparative Study of Dermal Pharmacokinetics Between Topical Drugs Using Open Flow Microperfusion in a Pig Model

PURPOSE

Accurate methods to determine dermal pharmacokinetics are important to increase the rate of clinical success in topical drug development. We investigated in an in vivo pig model whether the unbound drug concentration in the interstitial fluid as determined by dermal open flow microperfusion (dOFM) is a more reliable measure of dermal exposure compared to dermal biopsies for seven prescription or investigational drugs. In addition, we verified standard dOFM measurement using a recirculation approach and compared dosing frequencies (QD versus BID) and dose strengths (high versus low drug concentrations).

METHODS

Domestic pigs were topically administered seven different drugs twice daily in two studies. On day 7, drug exposures in the dermis were assessed in two ways: (1) dOFM provided the total and unbound drug concentrations in dermal interstitial fluid, and (2) clean punch biopsies after heat separation provided the total concentrations in the upper and lower dermis.

RESULTS

dOFM showed sufficient intra-study precision to distinguish interstitial fluid concentrations between different drugs, dose frequencies and dose strengths, and had good reproducibility between studies. Biopsy concentrations showed much higher and more variable values. Standard dOFM measurements were consistent with values obtained with the recirculation approach.

CONCLUSIONS

dOFM pig model is a robust and reproducible method to directly determine topical drug concentration in dermal interstitial fluid. Dermal biopsies were a less reliable measure of dermal exposure due to possible contributions from drug bound to tissue and drug associated with skin appendages.

Human Dose and Pharmacokinetic Predictions for Biologics at Boehringer Ingelheim: A Retrospective Analysis

INTRODUCTION

Accurate predictions of pharmacokinetics and efficacious doses for biologics in humans are critical for selecting appropriate first-in-human starting doses and dose ranges and for estimating clinical material needs and cost of goods. This also impacts clinical feasibility, particularly for subcutaneously administered biologics.

METHODS

We performed a comprehensive comparison between predicted and observed clearances and doses in humans for a set of 22 biologic drugs developed at Boehringer Ingelheim (BI) over the last 2 decades. The analysis included biologics across three therapeutic areas comprising a wide variety of modalities: mono- and bispecific monoclonal antibodies (mAbs) and nanobodies and a Fab fragment.

RESULTS

Our analysis showed that observed clearances in humans were within twofold of predicted clearances for 17 out of 20 biologics (85%). Six biologics had uncharacteristically high observed human clearances (range 32-280 mL/h) for their respective molecular classes, impacting their clinical developability. For three molecules, molecular characteristics contributed to the high clearance. Clinically selected doses were within twofold of predicted for 58% of projects. With 42% and 25% of projects selecting clinical doses higher than two- or threefold the predicted value, respectively, the importance of better understanding not only the pharmacokinetic (PK) but also the predictivity of pharmacodynamic models is highlighted.

CONCLUSIONS

We provide a clinical pharmacology perspective on the commonly accepted twofold range of human clearance predictions as well as the implications of higher than predicted targeted efficacious plasma concentration on clinical development. Finally, an analysis of key success factors for biologics at BI was conducted, which may be relevant for the entire pharmaceutical industry. This is one of the largest retrospective analyses for biologics and provides further evidence that successful predictions of human PK and efficacious dose will be further facilitated by gathering key translational data early in research.

Efficacy and safety of baricitinib in patients with severe COVID-19: A systematic review and meta-analysis

BACKGROUND

This study aimed to investigate the efficacy and safety of baricitinib in patients with severe coronavirus disease 2019 (COVID-19).

METHODS

Databases were searched for studies that compared the clinical efficacy and adverse effects of baricitinib with standard therapy for the treatment of severe COVID-19 and clearly reported relevant outcomes published until December 31, 2022. The corresponding data were extracted from these studies. A fixed-effects model was used to calculate the pooled estimates. The study protocol can be accessed at PROSPERO (CRD42023394173).

RESULTS

The baricitinib group had a significantly lower mortality rate and proportion of patients who received mechanical ventilation than the control group (OR = 0.61, 0.57; P = .008, 0.02; 95% CI 0.42-0.88; 0.35-0.92; I2 = 71% and 86%, respectively). The length of hospital stay and rates of severe adverse events were not significantly different between the 2 groups.

CONCLUSION

Baricitinib reduces mortality and mechanical ventilation requirements in patients with severe COVID-19. Therefore, we developed a comprehensive understanding of the role of baricitinib in patients with severe COVID-19.

Trends, challenges, and success factors in pharmaceutical portfolio management: Cognitive biases in decision-making and their mitigating measures

Effective portfolio management is crucial for innovation and sustaining revenue in pharmaceutical companies. This article holistically reviews trends, challenges, and approaches to pharmaceutical portfolio management and focuses, in particular, on cognitive biases in portfolio decision-making. Portfolio managers strongly rely on external innovation and face increasing competitive pressure and portfolio complexity. The ability to address biases and make robust decisions remains a challenge. Portfolio management practitioners most commonly face confirmation bias, champion bias, or misaligned incentives, which they seek to mitigate through expert input, team diversity, and rewarding truth-seeking. Ultimately, highest-quality portfolio management decision-making could be enabled by three factors: high-quality data, structured review processes, and comprehensive mitigating measures against biases in decision-making.

Experimental Medicine Approaches in Early-Phase CNS Drug Development

Traditionally, Phase 1 clinical trials were largely conducted in healthy normal volunteers and focused on collection of safety, tolerability, and pharmacokinetic data. However, in the CNS therapeutic area, with more drugs failing in later phase development, Phase 1 trials have undergone an evolution that includes incorporation of novel approaches involving novel study designs, inclusion of biomarkers, and early inclusion of patients to improve the pharmacologic understanding of novel CNS-active compounds early in clinical development with the hope of improving success in later phase pivotal trials. In this chapter, the authors will discuss the changing landscape of Phase 1 clinical trials in CNS, including novel trial methodology, inclusion of pharmacodynamic biomarkers, and experimental medicine approaches to inform early decision-making in clinical development.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a “good” hit in the course of a screening campaign still rely on a lot of luck – which is within the reach of academics –, that the hit to lead process requires a lot of chemistry and that if public–private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Paxlovid: Mechanism of Action, Synthesis, and In Silico Study

In this work, the discovery and description of PF-07321332, a major bioavailable oral SARS-CoV-2 protease inhibitor with in vitro human coronavirus antiviral activity, and excellent selection of off-target and in vivo immune profiles are reported. Various drugs and novel compound candidates for the treatment of the COVID-19 pandemic have been developed. PF-07321332 (or nirmatrelvir) is a new oral antiviral drug developed by Pfizer. In response to the pandemic, Pfizer has developed the COVID vaccine and in 2022 will launch its new major anti-SARS-Cov-2 protease inhibitor (PI). The combination of ritonavir and nirmatrelvir is under study in phase III of the clinical trial with a brand name Paxlovid. Paxlovid is an active 3Cl protease inhibitor. Paxlovid exerts its antiviral efficacy by inhibiting a necessary protease in the viral replication procedure. Proteases of coronavirus cleave several sites in the viral polyprotein where pyrrolidone was replaced by flexible glutamine. Due to the coronavirus pandemic, there is high demand for synthesis and development of this novel drug. Herein, we report the synthetic route and the mechanism of action was recently published on nirmatrelvir. Also, a comparison of the performance of two new oral antiviruses (molnupiravir and nirmatrelvir) for the treatment of COVID-19 is described. This review will be helpful for different disciplines such as biochemistry, organic chemistry, medicinal chemistry, and pharmacology.

Oral Nirmatrelvir/Ritonavir Therapy for COVID-19: The Dawn in the Dark?

Nirmatrelvir/ritonavir (Paxlovid™) is an effective and safe antiviral drug that inhibits the main protease (M^pro), 3CL protease, of SARS-CoV-2. A reduction in COVID-19-related hospitalization or death was observed in patients treated with nirmatrelvir/ritonavir within five days of symptom onset. Moreover, good oral availability enables the usage of nirmatrelvir/ritonavir, not only in hospitalized patients, but also among outpatients. Nirmatrelvir (PF-07321332) has been demonstrated to stop the spread of COVID-19 in animal models. Despite frequent mutations in the viral genomes of SARS-CoV-2, nirmatrelvir shows an effective antiviral effect against recent coronavirus mutants. Despite the promising antiviral effect of nirmatrelvir, there are several unresolved concerns. First, the final results of large-scale clinical trials for early therapy of mild cases of COVID-19 are not yet published. Second, the effectiveness of nirmatrelvir against upcoming variants in the coming years requires close monitoring. Considering the promising preliminary results of the EPIC-HR trial, nirmatrelvir/ritonavir in conjunction with vaccines and non-pharmacological interventions, may represent the dawn in the dark of the COVID-19 pandemic.

Development of novel ultra-sensitive IL-11 target engagement assays to support mechanistic PK/PD modeling for an anti-IL-11 antibody therapeutic

An in-house antibody generation campaign identified a diverse, high affinity set of anti-interleukin-11 (IL-11) monoclonal antibodies (mAbs) to enable successful development of novel, custom ultra-sensitive target engagement assays for detection of “free” (unbound to the dosed anti-IL-11 therapeutic mAb) and “total” (free and mAb-IL-11 complexed form) IL-11 in preclinical species and human. Antibody hits from distinct epitope communities were screened on various platforms, including enzyme-linked immunosorbent assay, Meso Scale Discovery, Simoa HD-1 and Simoa Planar Array (SP-X), and used for assay development and sensitivity optimization. The ultra-sensitive SP-X format achieved a lower limit of quantitation of 0.006 pg/mL, enabling the first reported baseline levels of IL-11 in healthy control plasma determined by custom bioanalytical assays. These newly established baseline levels supported mechanistic pharmacokinetic/pharmacodynamic modeling in mouse, cynomolgus monkey, and human for a greater understanding of preclinical study design and in vivo dynamic interaction of soluble IL-11 with an anti-IL-11 antibody therapeutic candidate. Modeling and simulation also helped refine the utility of assays with respect to their potential use as target engagement biomarkers in the clinic.

Abbreviations IL-11: Interleukin-11, TE: Target engagement, PK/PD: Pharmacokinetic/pharmacodynamic, mAb: Monoclonal antibody, NHP: Non-human primate, IgG: Immunoglobulin G, Cyno: Cynomolgulus monkey, GFR: Glomerular filtration rate, BQL: Below quantitation levels, DRM: Disease relevant model, kDa: kilodaltons, SPR: Surface plasmon resonance, pSTAT3: phosphorylated STAT3, IL-11R: Interleukin-11 receptor, TPP: Target product protein, LLOQ: Lower limit of quantitation, RLU: Relative light units