Introduction:Attention-deficit/hyperactivity disorder (ADHD) is an impairing psychiatric condition with the stimulants, lisdexamfetamine (LDX), and methylphenidate (MPH), as the first lines pharmacological treatment.

Methods: Herein, we applied a novel in silico method to evaluate virtual LDX (vLDX) and vMPH as treatments for ADHD applying quantitative systems pharmacology (QSP) models. The objectives were to evaluate the model’s output, considering the model characteristics and the information used to build them, to compare both virtual drugs’ efficacy mechanisms, and to assess how demographic (age, body mass index, and sex) and clinical characteristics may affect vLDX’s and vMPH’s relative efficacies.

Results and Discussion: : We molecularly characterized the drugs and pathologies based on a bibliographic search, and generated virtual populations of adults and children-adolescents totaling 2,600 individuals. For each virtual patient and virtual drug, we created physiologically based pharmacokinetic and QSP models applying the systems biology-based Therapeutic Performance Mapping System technology. The resulting models’ predicted protein activity indicated that both virtual drugs modulated ADHD through similar mechanisms, albeit with some differences. vMPH induced several general synaptic, neurotransmitter, and nerve impulse-related processes, whereas vLDX seemed to modulate neural processes more specific to ADHD, such as GABAergic inhibitory synapses and regulation of the reward system. While both drugs’ models were linked to an effect over neuroinflammation and altered neural viability, vLDX had a significant impact on neurotransmitter imbalance and vMPH on circadian system deregulation. Among demographic characteristics, age and body mass index affected the efficacy of both virtual treatments, although the effect was more marked for vLDX. Regarding comorbidities, only depression negatively impacted both virtual drugs’ efficacy mechanisms and, while that of vLDX were more affected by the co-treatment of tic disorders, the efficacy mechanisms of vMPH were disturbed by wide-spectrum psychiatric drugs. Our in silico results suggested that both drugs could have similar efficacy mechanisms as ADHD treatment in adult and pediatric populations and allowed raising hypotheses for their differential impact in specific patient groups, although these results require prospective validation for clinical translatability.

Background: Chronic spontaneous urticaria (CSU) is a rare, heterogeneous, severely debilitating, and often poorly controlled skin disease resulting in an itchy eruption that can be persistent. Antihistamines and omalizumab, an anti-IgE mAb, are the only licensed therapies. Although CSU pathogenesis is not yet fully understood, mast cell activation through the IgE:high-affinity IgE receptor (FcεRI) axis appears central to the disease process.

Objective: We sought to model CSU pathophysiology and identify in silico the mechanism of action of different CSU therapeutic strategies currently in use or under development.

Methods: Therapeutic performance mapping system technology, based on systems biology and machine learning, was used to create a CSU interactome validated with gene expression data from patients with CSU and a CSU model that was used to evaluate CSU pathophysiology and the mechanism of action of different therapeutic strategies.

Results: : Our models reflect the known role of mast cell activation as a central process of CSU pathophysiology, as well as recognized roles for different therapeutic strategies in this and other innate and adaptive immune processes. They also allow determining similarities and differences between them; anti-IgE and Bruton tyrosine kinase inhibitors play a more direct role in mast cell biology through abrogation of FcεRI signaling activity, whereas anti-interleukins and anti-Siglec-8 have a role in adaptive immunity modulation.

Conclusion: In silico CSU models reproduced known CSU and therapeutic strategies features. Our results could help advance understanding of therapeutic mechanisms of action and further advance treatment research by patient profile.

Acute respiratory distress syndrome (ARDS), associated with high mortality rate, affects up to 67% of hospitalized COVID-19 patients. Early evidence indicated that the pathogenesis of COVID-19 evoked ARDS is, at least partially, mediated by hyperinflammatory cytokine storm in which interleukin 6 (IL-6) plays an essential role. The corticosteroid dexamethasone is an effective treatment for severe COVID-19 related ARDS. However, trials of other immunomodulatory therapies, including anti-IL6 agents such as tocilizumab and sarilumab, have shown limited evidence of benefit as monotherapy. But recently published large trials have reported added benefit of tocilizumab in combination with dexamethasone in severe COVID-19 related ARDS. In silico tools can be useful to shed light on the mechanisms evoked by SARS-CoV-2 infection and of the potential therapeutic approaches. Therapeutic performance mapping system (TPMS), based on systems biology and artificial intelligence, integrate available biological, pharmacological and medical knowledge to create mathematical models of the disease. This technology was used to identify the pharmacological mechanism of dexamethasone, with or without tocilizumab, in the management of COVID-19 evoked ARDS. The results showed that while dexamethasone would be addressing a wider range of pathological processes with low intensity, tocilizumab might provide a more direct and intense effect upon the cytokine storm. Based on this in silico study, we conclude that the use of tocilizumab alongside dexamethasone is predicted to induce a synergistic effect in dampening inflammation and subsequent pathological processes, supporting the beneficial effect of the combined therapy in critically ill patients. Future research will allow identifying the ideal subpopulation of patients that would benefit better from this combined treatment.

Objective:: Real-world studies are needed to identify factors associated with response to biologic therapies in patients with axial spondyloarthritis (SpA). The objective was to assess sex differences in response to tumor necrosis factor inhibitors (TNFi) and to explore possible risk factors associated with TNFi efficacy.

Methods: A total of 969 patients with axial SpA (315 females, 654 males) enrolled in the BIOBADASER registry (2000-2019) who initiated a TNFi (first, second, or further lines) were studied. Statistical and artificial intelligence (AI)-based data analyses were used to explore the association of sex differences and other factors to TNFi response, using the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), to calculate the BASDAI50, with an improvement of at least 50% of the BASDAI score, and using the Ankylosing Spondylitis Disease Activity Score, calculated using the C-reactive protein level (ASDAS-CRP).

Results: : Females had a lower probability of reaching a BASDAI50 response with a first line TNFi treatment at the second year of follow-up (P = 0.018) and a lesser reduction of the ASDAS-CRP at this time point. The logistic regression model showed lower BASDAI50 responses to TNFi in females (P = 0.05). Other factors, such as older age (P = 0.004), were associated with unfavorable responses. The AI data analyses reinforced the idea that age at the beginning of the treatment was the main factor associated with an unfavorable response. The combination of age with other clinical characteristics (female sex or cardiovascular risk factors and events) potentially contributed to an unfavorable response to TNFi.

Conclusion: : In this national multicenter registry, female sex was associated with less response to a first-line TNFi by the second year of follow-up. A higher age at the start of the TNFi was the main factor associated with an unfavorable response to TNFi.

Clinical evidence supports the combination of cabozantinib with an immune checkpoint inhibitor for the treatment of metastatic clear cell renal cell carcinoma (mccRCC) and suggests a synergistic antitumour activity of this combination. Nevertheless, the biological basis of this synergy is not fully characterized. We studied the mechanisms underpinning the potential synergism of cabozantinib combined with a PD1 inhibitor in mccRCC and delved into cabozantinib monotherapy properties supporting its role to partner these combinations. To model physiological drug action, we used a machine learning-based technology known as Therapeutic Performance Mapping Systems, applying two approaches: Artificial Neural Networks and Sampling Methods. We found that the combined therapy was predicted to exert a wide therapeutic action in the tumour and the microenvironment. Cabozantinib may enhance the effects of PD1 inhibitors on immunosurveillance by modulating the innate and adaptive immune system, through the inhibition of VEGF-VEGFR and Gas6- AXL/TYRO3/MER (TAM) axes, while the PD1 inhibitors may boost the antiangiogenic and pro–apoptotic effects of cabozantinib by modulating angiogenesis and T-cell cytotoxicity. Cabozantinib alone was predicted to restore cellular adhesion and hamper tumour proliferation and invasion. These data provide a biological rationale and further support for cabozantinib plus PD1 inhibitor combination and may guide future nonclinical and clinical research.

Regulatory agencies encourage computer modeling and simulation to reduce the time and cost of clinical trials. Although still not classified in formal guidelines, system biology-based models represent a powerful tool for generating hypotheses with great molecular detail. Herein, we have applied a mechanistic head-to-head in silico clinical trial (ISCT) between two treatments for attention-deficit/hyperactivity disorder, to wit lisdexamfetamine (LDX) and methylphenidate (MPH). The ISCT was generated through three phases comprising (i) the molecular characterization of drugs and pathologies, (ii) the generation of adult and children virtual populations (vPOPs) totaling 2,600 individuals and the creation of physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models, and (iii) data analysis with artificial intelligence methods. The characteristics of our vPOPs were in close agreement with real reference populations extracted from clinical trials, as did our PBPK models with in vivo parameters. The mechanisms of action of LDX and MPH were obtained from QSP models combining PBPK modeling of dosing schemes and systems biology-based modeling technology, i.e., therapeutic performance mapping system. The step-by-step process described here to undertake a head-to-head ISCT would allow obtaining mechanistic conclusions that could be extrapolated or used for predictions to a certain extent at the clinical level. Altogether, these computational techniques are proven an excellent tool for hypothesis-generation and would help reach a personalized medicine.

Background: Systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still’s disease (AOSD) are manifestations ofan autoinflammatory disorder with complex pathophysiology and significant morbidity, together also termed Still’sdisease. The objective of the current study is to set in silico models based on systems biology and investigate theoptimal treat-to-target strategy for Still’s disease as a proof-of-concept of the modeling approach
Methods: Molecular characteristics of Still’s disease and data on biological inhibitors of interleukin (IL)-1 (anakinra,canakinumab), IL-6 (tocilizumab, sarilumab), and glucocorticoids as well as conventional disease-modifying antirheumatic drugs (DMARDs, methotrexate) were used to construct in silico mechanisms of action (MoA) models bymeans of Therapeutic Performance Mapping System (TPMS) technology. TPMS combines artificial neuronal networks, sampling-based methods, and artificial intelligence. Model outcomes were validated with published expression data from sJIA patients.
Results: Biologicals demonstrated more pathophysiology-directed efficiency than non-biological drugs. IL-1 blockade mainly acts on proteins implicated in the innate immune system, while IL-6 signaling blockade has a weaker effect on innate immunity and rather affects adaptive immune mechanisms. The MoA models showed that in the autoinflammatory/systemic phases of Still’s disease, in which the innate immunity plays a pivotal role, the IL1β-neutralizing antibody canakinumab is more efficient than the IL-6 receptor-inhibiting antibody tocilizumab. MoA models reproduced 67% of the information obtained from expression data.
Conclusions: Systems biology-based modeling supported the preferred use of biologics as an immunomodulatory treatment strategy for Still’s disease. Our results reinforce the role for IL-1 blockade on innate immunity regulation, which is critical in systemic autoinflammatory diseases. This further encourages early use on Still’s disease IL-1 blockade to prevent the development of disease or drug-related complications. Further analysis at the clinical level will validate the findings and help determining the timeframe of the window of opportunity for canakinumab treatment.
Keywords: Systems biology, Systemic juvenile idiopathic arthritis, Adult-onset Still’s disease, Treat-to-target, Artificial intelligence, Machine learning, Personalized medicine

Unveiling the mechanism of action of a drug is key to understand the benefits and adverse reactions of a medication in an organism. However, in complex diseases such as heart diseases there is not a unique mechanism of action but a wide range of different responses depending on the patient. Exploring this collection of mechanisms is one of the clues for a future personalized medicine. The Therapeutic Performance Mapping System (TPMS) is a Systems Biology approach that generates multiple models of the mechanism of action of a drug. Each molecular mechanism generated could be associated to particular individuals, here defined as prototype-patients, hence the generation of models using TPMS technology may be used for detecting adverse effects to specific patients. TPMS operates by (1) modelling the responses in humans with an accurate description of a protein network and (2) applying a Multilayer Perceptron-like and sampling strategy to find all plausible solutions. In the present study, TPMS is applied to explore the diversity of mechanisms of action of the drug combination sacubitril/valsartan. We use TPMS to generate a wide range of models explaining the relationship between sacubitril/valsartan and heart failure (the indication), as well as evaluating their association with macular degeneration (a potential adverse effect). Among the models generated, we identify a set of mechanisms of action associated to a better response in terms of heart failure treatment, which could also be associated to macular degeneration development. Finally, a set of 30 potential biomarkers are proposed to identify mechanisms (or prototype-patients) more prone of suffering macular degeneration when presenting good heart failure response. All prototype-patients models generated are completely theoretical and therefore they do not necessarily involve clinical effects in real patients. Data and accession to software are available at http://sbi.upf.edu/data/tpms/

Identifying immune correlates of protection and mechanisms of immunity accelerates and streamlines the development of vaccines. RTS,S/AS01E, the most clinically advanced malaria vaccine, has moderate efficacy in African children. In contrast, immunization with sporozoites under antimalarial chemoprophylaxis (CPS immunization) can provide 100% sterile protection in naïve adults. We used systems biology approaches to identifying correlates of vaccine-induced immunity based on transcriptomes of peripheral blood mononuclear cells from individuals immunized with RTS,S/AS01E or chemoattenuated sporozoites stimulated with parasite antigens in vitro. Specifically, we used samples of individuals from two age cohorts and three African countries participating in an RTS,S/AS01E pediatric phase 3 trial and malaria-naïve individuals participating in a CPS trial. We identified both preimmunization and postimmunization transcriptomic signatures correlating with protection. Signatures were validated in independent children and infants from the RTS,S/AS01E phase 3 trial and individuals from an independent CPS trial with high accuracies (>70%). Transcription modules revealed interferon, NF-κB, Toll-like receptor (TLR), and monocyte-related signatures associated with protection. Preimmunization signatures suggest that priming the immune system before vaccination could potentially improve vaccine immunogenicity and efficacy. Last, signatures of protection could be useful to determine efficacy in clinical trials, accelerating vaccine candidate testing. Nevertheless, signatures should be tested more extensively across multiple cohorts and trials to demonstrate their universal predictive capacity. http://sbi.upf.edu/data/tpms/

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

Chronic lymphocytic leukemia (CLL) is a B lymphoid malignancy highly dependent on the microenvironment. Despite new targeted therapies such as ibrutinib and venetoclax, disease progression and relapse remain an issue. CLL cell interactions with the supportive tissue microenvironment play a critical role in disease pathogenesis. We used a platform for drug discovery based on systems biology and artifcial intelligence, to identify drugs targeting key proteins described to have a role in the microenvironment. The selected compounds were screened in CLL cell lines in the presence of stromal cells to mimic the microenvironment and validated the best candidates in primary CLL cells. Our results showed that the commercial drug simvastatin was the most efective and selective out of the tested compounds. Simvastatin decreased CLL cell survival and proliferation as well as cell adhesion. Importantly, this drug enhanced the antitumor efect of venetoclax and ibrutinib. We proposed that systems biology approaches combined with pharmacological screening could help to fnd new drugs for CLL treatment and to predict new combinations with current therapies. Our results highlight the possibility of repurposing widely used drugs such as statins to target the microenvironment and to improve the efcacy of ibrutinib or venetoclax in CLL cells.