Physiotarget is a bioinformatics company devoted to developing new technologies for Cancer Theranostics, MDR Bacteria characterization, and Pharmacovigilance
We are a technology company that develops innovative solutions for relevant public health problems. The company was created in 2019 by scientists with extensive experience in the bioinformatics sector. Physiotarget main areas of expertise are:
Our focus is to improve patients’ health conditions with solid tumors, using next-generation sequencing (NGS) and other advanced technologies such as high-performance computing. Our expertise is to deploy scalable, secure IT solutions based on mathematical and computational modeling of cancer tumors. We aim at personalized medicine approaches to individualized precision treatment and a high level of specificity. Our technological development seeks to minimize the side effects of diseases, particularly cancer, and optimize the administration and chemotherapies’ composition, giving a better life to the patients.
In other words, we aim for solutions and innovations in the sector of cancer, adding treatment options that improve benefits to patients so as the quality of life and comfort. Our main product under development is Terafen®, whose purpose is to combine RNA sequencing (RNA-seq) to measure the level of gene expression and the complexity of the signaling network tumor assessed by reference to the interactome. Terafen® identifies the genes differentially expressed in tumors and the corresponding drugs capable of inhibiting their associated proteins.
A bioinformatics pipeline automates the process that analyzes RNA-seq (deep sequencing) or Ampliseq (gene panel) data to determine the most desirable drug combinations for each case. The Doctor receives an analysis report and can choose the most appropriate cocktail for the type of tumor considered, taking into account each patient’s characteristics.
Therefore, with Terafen®, it will be possible to improve patients’ conditions with solid tumors, minimize side effects, increase patient compliance to treatment, improve the therapeutic result’s effectiveness, and reduce the costs associated with palliative treatment.
Healthcare-associated infections (HAIs), previously called hospital infections, are a severe public health problem and can develop either as a direct result of medical or surgical treatment or in contact with a healthcare setting. These infections include central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia (VAP), and surgical site infections. Among the pathogens related to HAI, the group of bacteria is the one that stands out. More than 2 million HAIs occur each year in the USA (Stone et al., 2005), with 50–60% being caused by antimicrobial-resistant bacteria. Given the potential severity of multidrug-resistant (MDR) bacteria and the lack of treatment options, identifying and implementing effective strategies to prevent such infections are urgent priorities. At Physiotarget, we employ state-of-the-art Bioinformatics methods and technologies to characterize MDR bacteria and determine the best treatment options.
During the post-marketing period, when medicines are used by large population contingents and for more extended periods, adverse events (AE) can alter the drug’s risk-benefit ratio enough to require regulatory action. AE is defined as health problems that can emerge in a user or patient during treatment with a pharmaceutical product, potentially resulting from medication errors, deviation in the drugs’ quality, adverse drug reactions (ADR), drug-drug interactions, intoxications.
According to the World Health Organization (WHO), pharmacovigilance is defined as “as the science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problem“. Pharmacovigilance is responsible for identifying, assessing, and monitoring drug-related AE’s occurrence to guarantee that the benefits outweigh the risks caused by them. The main instrument in pharmacovigilance is spontaneous reporting, informing government agencies on AE that have occurred with the drugs’ use to achieve this objective.
Computational methods commonly referred to as “signal detection” allow drug safety evaluators to analyze large data volumes to identify risk signals for potential AE and serve as an essential component of pharmacovigilance. These signals alone are not sufficient to establish a causal relationship, but they are considered early warnings that require in-depth assessment by specialists to establish causality. At Physiotarget, we develop scalable signal detection systems that integrate data from multiple sources in real-time, combined with statistical analysis methods to identify the most relevant signals.