Nephropathic Cystinosis: Pathogenic Roles of Inflammation and Potential for New Therapies

The activation of several inflammatory pathways has recently been documented in patients and different cellular and animal models of nephropathic cystinosis. Upregulated inflammatory signals interact with many pathogenic aspects of the disease, such as enhanced oxidative stress, abnormal autophagy, inflammatory cell recruitment, enhanced cell death, and tissue fibrosis. Cysteamine, the only approved specific therapy for cystinosis, ameliorates many but not all pathogenic aspects of the disease. In the current review, we summarize the inflammatory mechanisms involved in cystinosis and their potential impact on the disease pathogenesis and progression. We further elaborate on the crosstalk between inflammation, autophagy, and apoptosis, and discuss the potential of experimental drugs for suppressing the inflammatory signals in cystinosis.

Oncologists’ reflections on patient rights and access to compassionate use drugs: A qualitative interview study from an academic cancer center

The U.S. Food and Drug Administration (FDA) allows patients with serious illnesses to access investigational drugs for “compassionate use” outside of clinical trials through expanded access (EA) Programs. The federal Right-to-Try Act created an additional pathway for non-trial access to experimental drugs without institutional review board or FDA approval. This removal of oversight amplifies the responsibility of physicians, but little is known about the role of practicing physicians in non-trial access to investigational drugs. We undertook semi-structured interviews to capture the experiences and opinions of 21 oncologists all with previous EA experience at a major cancer center. We found five main themes. Participants with greater EA experience reported less difficulty accessing drugs through the myriad of administrative processes and drug company reluctance to provide investigational products while newcomers reported administrative hurdles. Oncologists outlined several rationales patients offered when seeking investigational drugs, including those with stronger health literacy and a good scientific rationale versus others who remained skeptical of conventional medicine. Participants reported that most patients had realistic expectations while some had unrealistic optimism. Given the diverse reasons patients sought investigational drugs, four factors—scientific rationale, risk-benefit ratio, functional status of the patient, and patient motivation—influenced oncologists’ decisions to request compassionate use drugs. Physicians struggled with a “right-to-try” framing of patient access to experimental drugs, noting instead their own responsibility to protect patients’ best interest in the uncertain and risky process of off-protocol access. This study highlights the willingness of oncologists at a major cancer center to pursue non-trial access to experimental treatments for patients while also shedding light on the factors they use when considering such treatment. Our data reveal discrepancies between physicians’ sense of patients’ expectations and their own internal sense of professional obligation to shepherd a safe process for patients at a vulnerable point in their care.

A Deep-Learning Proteomic-Scale Approach for Drug Design

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

Acute respiratory distress syndrome in COVID-19: possible mechanisms and therapeutic management

COVID-19 pandemic is a serious concern in the new era. Acute respiratory distress syndrome (ARDS), and lung failure are the main lung diseases in COVID-19 patients. Even though COVID-19 vaccinations are available now, there is still an urgent need to find potential treatments to ease the effects of COVID-19 on already sick patients. Multiple experimental drugs have been approved by the FDA with unknown efficacy and possible adverse effects. Probably the increasing number of studies worldwide examining the potential COVID-19 related therapies will help to identification of effective ARDS treatment. In this review article, we first provide a summary on immunopathology of ARDS next we will give an overview of management of patients with COVID-19 requiring intensive care unit (ICU), while focusing on the current treatment strategies being evaluated in the clinical trials in COVID-19-induced ARDS patients.

Pharmacological Treatment Options for Coronavirus Disease-19 in Renal Patients

Patients with chronic kidney disease (CKD), including dialysis and transplant patients, are at greater risk of contracting SARS-CoV-2 due to kidney dysfunction and preexisting comorbidities. To date, a specific guideline on managing these high-risk patients infected with COVID-19 has not been established. As the current management of COVID-19 comprises mainly experimental drugs, the authors aim to provide information on dosing adjustments at different stages of kidney dysfunction and notable renal side effects. We performed a nonsystematical review of currently available COVID-19 drugs exploring several different clinical trial databases and search browsers. Several antivirals and monoclonal antibodies used in COVID-19 treatment require dosage adjustments in kidney dysfunction. In a global pandemic setting, nephrologists need to consider the appropriate dosage according to the renal function and closely monitor the side effects of different drug combinations to obtain the optimum therapeutic effect while avoiding further renal damage. Further studies are required to determine the safety and efficacy of these drugs in renal patients.

Corticosteroid Resistant Pulmonary Fibrosis: Pathophysiology and Management

Pulmonary fibrosis is a disease of the lower respiratory system. It might be as Idiopathic fibrosis which is obscure reason for disease or might be as an optional impact from different causes, for example, the environmental causes, for example, toxins and smoking, some connective tissue illnesses., infection diseases, for example, tuberculosis (TB) and corona virus, a few medications, for example, bleomycin, methotrexate, and radiation treatment. Glucocorticoid are used for treating inflammatory and immune diseases, like asthma, but interstitial lung disease, cystic fibrosis, and chronic obstructive pulmonary disease (COPD) at some stage, may become resistant to corticosteroid treatment. Glucocorticoids inhibit inflammation by many mechanisms. The oxidative stress leads to significantly decrease in activity and expression of Histone deacetylase 2 (HDAC-2) which causes resistant to the action of glucocorticoid. However, the dissociated glucocorticoids have been developed to decrease side effects, the dissociated glucocorticoid receptor agonists (DIGRAs) are a class of experimental drugs designed to share many of the desirable anti-inflammatory, immunosuppressive, or anticancer properties of classical glucocorticoid drugs with fewer side effects, but it is so difficult to dissociate anti-inflammatory effects from adverse effects. Patients with glucocorticoid resistance must use alternative anti-inflammatory treatments as well as drugs that may reverse the molecular mechanism of glucocorticoid resistant. Objective: This paper is to review the corticosteroid resistant pulmonary fibrosis and how overcome this resistance. The data was collected from December 2020 to September 2021.

A Deep Learning Proteomic Scale Approach for Drug Design

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multi-target therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach by computing interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning based autoencoder to first reduce the dimensionality of CANDO computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this model, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds are predicted to be significantly (p-value ≤ .05) more behaviorally similar relative to all corresponding controls, and 20/20 are predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design perform significantly better than those derived from natural sources (p-value ≤.05), suggesting that the model has learned an abstraction of rational drug design. We also show that designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhance thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. This work represents a significant step forward in automating holistic therapeutic design with machine learning, and subsequently offers a reduction in the time needed to generate novel, effective, and safe drug leads for any indication.

Computationally Repurposing Drugs for Breast Cancer Subtypes Using a Network-Based Approach

'De novo' drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called 'in silico' drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging.We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes.

Health emergencies and interoceptive sensibility modulate the perception of non-evidence-based drug use: Findings from the COVID-19 outbreak

Scientific evidence plays an important role in the therapeutic decision-making process. What happens when physicians are forced to make therapeutic decisions under uncertainty? The absence of scientific guidelines at the beginning of a pandemic due to an unknown virus, such as COVID-19, could influence the perceived legitimacy of the application of non-evidence-based therapeutic approaches. This paper reports on a test of this hypothesis, in which we administered an ad hoc questionnaire to a sample of 64 Italian physicians during the first wave of the COVID-19 pandemic in Italy (April 2020). The questionnaire statements regarding the legitimacy of off-label or experimental drugs were framed according to three different scenarios (Normality, Emergency and COVID-19). Furthermore, as the perception of internal bodily sensations (i.e., interoception) modulates the decision-making process, we tested participants’ interoceptive sensibility using the Multidimensional Assessment of Interoceptive Awareness (MAIA). The results showed that participants were more inclined to legitimate non-evidence-based therapeutic approaches in the COVID-19 and Emergency scenarios than the Normality scenario. We also found that scores on the MAIA Trusting subscale positively predicted this difference. Our findings demonstrate that uncertain medical scenarios, involving a dramatic increase in patient volume and acuity, can increase risk-taking in therapeutic decision-making. Furthermore, individual characteristics of health care providers, such as interoceptive ability, should be taken into account when constructing models to prevent the breakdown of healthcare systems in cases of severe emergency.

Bioethical Quandaries during the Period of a Pandemic

The period of the pandemic gave rise to multiple and intractable bioethical quandaries arising. In the context of the present study, we will limit ourselves to the examination of the critical issues of mandatory vaccination to manage the pandemic; compulsory medical testing, including temperature screening of the population; the use of experimental drugs; making the wearing of face masks mandatory; and the individual responsibility of each of us for the prevention of the pandemic. Participation stresses the importance of education in bioethics. Accordingly, it supports the notion that, once we win the fight for life and health, constitutional lawyers ought to take the reins and determine that the character of restrictive measures and healthcare policies adopted in periods of crisis, when a prime opportunity for their formulation presented itself because of the pandemic, is one of extraordinariness.