Quantitative systems modeling approaches towards model-informed drug development: Perspective through case studies

Quantitative systems pharmacology (QSP) modeling has become an increasingly popular approach impacting our understanding of disease mechanisms and helping predict patients’ treatment responses to facilitate study design or development go/no-go decisions. In this paper, we highlight the notable contributions and opportunities that QSP approaches are to offer during the drug development process by sharing three examples that have facilitated internal decisions. The barriers to successful applications and the factors that facilitate the success of the modeling approach is discussed

Treatment of Low-Blast Count AML using Hypomethylating Agents

In 2002, the WHO classification reduced the proportion of blasts in the bone marrow (BM) necessary for the diagnosis of acute myeloid leukemia (AML) from 30% to 20%, eliminating the RAEB-t subtype of myelodysplastic syndromes (MDS). However, this AML subtype, defined as low-blast count AML (LBC-AML, with 20-30% BM-blasts) is characterized by peculiar features, as increased frequency in elderly individuals and after cytotoxic treatment for a different primary disease (therapy-related), poor-risk cytogenetics, lower white blood cell counts, and less frequent mutations of NPM1 and FLT3 genes. The clinical course of this entity is often similar to MDS with 10-19% BM-blasts. The hypomethylating agents azacitidine and decitabine have been shown to induce responses and prolong survival both in MDS and LBC-AML.  The role of these agents has been also demonstrated in AML with >30% BM-blasts, particularly in patients with poor-risk cytogenetics and in AML with myelodysplasia related changes. Most recent studies are evaluating strategies to improve outcome, including combinations of hypomethylating agents with immune-response checkpoint inhibitors, which have a role in cancer immune surveillance. Efforts are also ongoing to identify mutations which may predict response and survival in these patients

Retinoic acid-induced 1 gene haploinsufficiency alters lipid metabolism and causes autophagy defects in Smith-Magenis syndrome

Smith-Magenis syndrome (SMS) is a neurodevelopmental disorder characterized by cognitive and behavioral symptoms, obesity, and sleep disturbance, and no therapy has been developed to alleviate its symptoms or delay disease onset. SMS occurs due to haploinsufficiency of the retinoic acid-induced-1 (RAI1) gene caused by either chromosomal deletion (SMS-del) or RAI1 missense/nonsense mutation. The molecular mechanisms underlying SMS are unknown. Here, we generated and characterized primary cells derived from four SMS patients (two with SMS-del and two carrying RAI1 point mutations) and four control subjects to investigate the pathogenetic processes underlying SMS. By combining transcriptomic and lipidomic analyses, we found altered expression of lipid and lysosomal genes, deregulation of lipid metabolism, accumulation of lipid droplets, and blocked autophagic flux. We also found that SMS cells exhibited increased cell death associated with the mitochondrial pathology and the production of reactive oxygen species. Treatment with N-acetylcysteine reduced cell death and lipid accumulation, which suggests a causative link between metabolic dyshomeostasis and cell viability. Our results highlight the pathological processes in human SMS cells involving lipid metabolism, autophagy defects and mitochondrial dysfunction and suggest new potential therapeutic targets for patient treatment

Choice of Frontline Tyrosine-Kinase Inhibitor and Early Events in Very Elderly Patients With Chronic Myeloid Leukemia in Chronic Phase: A "Campus CML" Study

Objectives: The study aimed to evaluate the utilization of frontline TKI therapy in a large cohort of elderly CP-CML patients. Methods: A retrospective analysis was conducted on 332 CP-CML patients aged 75 years or older among 1929 diagnosed from January 2012 to December 2019 followed at 36 participating Hematology Centers involved in the "Campus CML" project. Results: Among the patients analyzed, 85.8% received imatinib (IM) while 14.2% received second-generation TKIs (2G-TKI), 59.5% dasatinib, and 40.5% nilotinib. Most patients initiated IM at standard dose (67.3%) while 32.7% at reduced dose. A similar trend was observed with 2G-TKIs. The cumulative incidence of permanent TKI discontinuation at 12 months was 28.4%, primarily due to primary resistance (10.1%) and extra-hematologic toxicity (9.5%), with no significant difference between IM and 2G-TKI groups. Following the introduction of generic IM in Italy in 2018, IM usage increased significantly compared with 2G-TKIs. Conclusions: IM was in our Centers the preferred frontline therapy for older CP-CML patients, with increasing utilization after the introduction of generic formulations. However, 2G-TKIs are still used in a substantial proportion of patients, suggesting individualized physician assessments regarding patient suitability and expectations. Further investigation is needed to assess efficacy and safety of reduced TKI doses in this patient population

Frequency of left ventricular hypertrophy in non-valvular atrial fibrillation

Left ventricular hypertrophy (LVH) is significantly related to adverse clinical outcomes in patients at high risk of cardiovascular events. In patients with atrial fibrillation (AF), data on LVH, that is, prevalence and determinants, are inconsistent mainly because of different definitions and heterogeneity of study populations. We determined echocardiographic-based LVH prevalence and clinical factors independently associated with its development in a prospective cohort of patients with non-valvular (NV) AF. From the "Atrial Fibrillation Registry for Ankle-brachial Index Prevalence Assessment: Collaborative Italian Study" (ARAPACIS) population, 1,184 patients with NVAF (mean age 72 \ub1 11 years; 56% men) with complete data to define LVH were selected. ARAPACIS is a multicenter, observational, prospective, longitudinal on-going study designed to estimate prevalence of peripheral artery disease in patients with NVAF. We found a high prevalence of LVH (52%) in patients with NVAF. Compared to those without LVH, patients with AF with LVH were older and had a higher prevalence of hypertension, diabetes, and previous myocardial infarction (MI). A higher prevalence of ankle-brachial index 640.90 was seen in patients with LVH (22 vs 17%, p = 0.0392). Patients with LVH were at significantly higher thromboembolic risk, with CHA2DS2-VASc 652 seen in 93% of LVH and in 73% of patients without LVH (p <0.05). Women with LVH had a higher prevalence of concentric hypertrophy than men (46% vs 29%, p = 0.0003). Logistic regression analysis demonstrated that female gender (odds ratio [OR] 2.80, p <0.0001), age (OR 1.03 per year, p <0.001), hypertension (OR 2.30, p <0.001), diabetes (OR 1.62, p = 0.004), and previous MI (OR 1.96, p = 0.001) were independently associated with LVH. In conclusion, patients with NVAF have a high prevalence of LVH, which is related to female gender, older age, hypertension, and previous MI. These patients are at high thromboembolic risk and deserve a holistic approach to cardiovascular prevention

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

Looking beyond the hype : applied AI and machine learning in translational medicine

Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. However, ML technologies do not come without their limitations and shortcomings. Current technical limitations and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale